TH

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Division of Informatics, University of Edinburgh

Equation-Based and Agent-Based Modeling of Supply Networks

by

Wanwimol Thawornchak

Informatics Research Report EDI-INF-RR-0045

Division of Informatics September 2001http://www.informatics.ed.ac.uk/

Equation-Based and

Agent-Based Modeling

of Supply Networks

Wanwimol Thawornchak

MSc in Computer Science Division of Informatics

University of Edinburgh 2001

Abstract

“Supply networks” is an approach to deal with the interconnections of organizations for

the purpose of supplying goods and services required by consumers. Emerging e-

commerce increases its important because a supply network is directly applicable to

business-to-business scenarios. This project aims to investigate the dynamics of supply

networks using two modeling approaches, which have different but complementary

feature and compare and contrast them. The first is equation-based modeling, which is

the traditional modeling for system dynamics and uses a set of equations in order to

represent the system. The second is agent-based modeling, which consists of collections

of programs running in a distributed fashion and exchanging messages.

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Acknowledgements I would like to thank my supervisor, Wamberto W. Vasconcelos, for the special guidance

during the course of the research reported in this dissertation. I deeply appreciate the

many hours of thoughtful discussion and his seemingly endless patience. He was always

willing to help when I had problems and supported the idea behind this project from the

very beginning. I sincerely thank you.

I also would like to thank the Royal Thai Government for granting me a scholarship,

which gives me an opportunity to pursue my education and enhance my knowledge.

Thanks to Jarassri – my friend, who introduces this university to me and gives me much

advice during my studying.

Special thanks to Tanitha – my sister here, and our private Thai community at Richmond

Place for all encouragement and warm support for the time of being an oversea student.

Lastly and most importantly, my family-especially my parents and my sisters -who give

me the unconditional and unbounded support. Their endless love, devotion, frequent

phone calls have encouraged and cheered me up. I thank you for always being with me.

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Table of Contents

1. Introduction 1

1.1The purpose of the project 1

1.2 Methodology 1

1.3 A dissertation Layout 2

2. Supply Networks 3

2.1 Supply Networks: a Definition 3

2.2 The Network Perspective 5

2.3 Activities within Supply Networks 6

2.3.1 Channel Management 6

2.3.2 Material Management 7

2.3.3 Supply Chain Management 8

2.3.4 Lean Supply 9

2.4 Insight supply networks 10

2.4.1 Structure 10

2.4.2 Strategy 10

2.4.3 Relationships 11

2.4.4 Dynamics of Supply Networks 12

2.5 Summary 12

2.6 Supply Networks in the experiments 13

3. Modeling and simulation 14

3.1 Equation-Based Model 15

3.1.1 System Dynamics 15

3.1.2 Dynamic model 16

3.1.3 SIMILE 18

3.1.4 VenSim Simulation 21

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3.2 Agent-Based Model 24

3.2.1Simulation of Order Fulfillment in Divergent Assembly Supply Chains25

3.2.2 The DASCh Project 27

4. Experiments and Results 30 4.1 The first experiment 30

4.1.1 An equation-based model for supply networks 30

4.1.2 An agent-based model for supply networks 36

4.1.3 Compare and contrast the experimental results 46

4.2 The second experiment 48

4.2.1 An equation-based model for supply networks 48

4.2.2 An agent-based model for supply networks 51

4.2.3 Compare and contrast the experimental results 53

4.3 The third experiment 55

4.3.1 An equation-based model for supply networks 55

4.3.2 An agent-based model for supply networks 58

4.3.3 Compare and contrast the experimental results 60

5. Conclusions and Future Work 61 5.1 Conclusions 61

5.2 Future Work 64

Appendix A 70

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List of Figures

Figure 2-1: An “Hourglass” supply network 5

Figure 2-2: Activities within Supply Networks 6

Figure 2-3: Our 3-tier Supply Networks 13

Figure 3-1: Flow diagram along with the corresponding feedback loop 17

Figure 3-2: Model of population dynamics 19

Figure 3-3: Total population size 20

Figure 3-4: Population size of 4-age classes 20

Figure 3-5: System Dynamics (ODE) Model 22

Figure 3-6: Inventory Oscillation in EBM 23

Figure 3-7: SCN Agent Interactions in the Swarm Implementation 26

Figure 3-8: The DASCh Supply Chain 28

Figure 4-1: EBM for Supply Networks : Experiment 1 31

Figure 4-2: Simile Graphs1 : Experiment 1 33

Figure 4-3: Simile Graphs2 : Experiment 1 35

Figure 4-4: Simile Graphs3 : Experiment 1 36

Figure 4-5: ABM Lpad Platform 37

Figure 4-6: Supply Networks for ABM 38

Figure 4-7: A message-passing Diagram 41

Figure 4-8: ABM Graph1 : Experiment 1 43

Figure 4-9: ABM Graph2 : Experiment 1 45

Figure 4-10: Comparison for EBM and ABM graphs : Experiment 1 47

Figure 4-11: EBM for Supply Networks : Experiment 2 49

Figure 4-12: Average price from EBM : Experiment 2 50

Figure 4-13: Average price from ABM : Experiment 2 52

Figure 4-14: Average price of each dealer from ABM : Experiment 2 53

Figure 4-15: Comparison for EBM and ABM graphs : Experiment 2 54

Figure 4-16: EBM for Supply Networks : Experiment 3 56

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Figure 4-17: EBM graph 1 : Experiement3 57

Figure 4-18: EBM graph 2 : Experiment 3 58

Figure 4-19: ABM graph 1 : Experiment 3 59

Figure 4-20: Comparison for EBM and ABM graphs : Experiment 3 60

1

Chapter 1: Introduction

1.1 The purpose of the project

Modeling and simulation is a very active branch of computer science. This approach is

to create a model representing the real system and execute the model to observe its

behaviour experimentally. In our project, we propose two modeling approaches, which

are equation-based modeling (EBM) and agent-based modeling (ABM). EBM

represents the system as a set of equation that relates observables to one another. ABM

represents the system as the internal behaviour of each individual. We exploit two

models in order to study and analyse a supply network scenario, which is said dynamic.

Supply networks are the interconnections of entities for the purpose of supplying goods

and services required by end customers. The idea is to construct the relationships of

organisations between suppliers and consumers and its goal is to communicate

efficiently and consistently across boundaries. The supply network becomes important

because of its direct applicability to business-to-business scenarios, which are

commonplace in e-commerce.

1.2 Methodology

We construct EBM and ABM for our supply network scenario, which is created, based

on basic business principles. The scenario involves consumers, dealers and a producer,

who sell and buy products. For EBM, we use Simile, a graphic simulation and

modeling software developed by the Institute of Ecology and Resource Management,

The University of Edinburgh. For ABM, we use Sicstus Prolog in order to develop

agents that correspond to entities of our supply networks with support tools. The tool,

developed by Wamberto W. Vasconcelos, supports the replication of agents, and the

automatic upload and execution of a number of agents based on distributed simulation.

2

In analysing the result of the experiment, we use graphs from Simile and create new

graphs from collections of the data by using MS Excel and Matlab.

1.3 A dissertation Layout

This paper is composed of 5 chapters. Chapter 1 is the introduction, where an overview

of the project is given.

Chapter 2 is the surveys work on supply networks. We give the definition and literature

review of supply networks. There are also interesting topics related to supply networks

and a briefly explanation of the supply network scenario that will be used in our model.

Chapter 3 is the overview of the modeling and simulation. We give the meaning of

model and simulation and introduce two approaches of modeling, EBM and ABM.

EBM is the conventional model for mathematics and system dynamics discipline. ABM

is a new kind of modeling that there is the motivation for many researches to exploit

with dynamic works such as business area. We also introduce the interesting works

developed by EBM and ABM. There are the works of DASCh project involving using

EBM and ABM for supply networks, the work of Troy J. Strader, Fu-Ren Lin and

Michael J. Shaw using ABM for inventory scenario and the example of EBM in

ecology field from Simile software.

Chapter 4 is our models and simulation. We describe the model that is constructed by

EBM and ABM including a scenario. Three experiments are conducted by using our

models and their results are analysed.

Chapter 5 is the conclusions, which compare and contrast employing EBM and ABM

for business scenario such as supply networks.

3

Chapter 2: Supply Networks

In our experiments with the business scenario, we focus on supply networks, a popular

and interesting approach to today’s business. Computers and Internet influence in

commercial world and has resulted in the emergence of electronic business (e-business)

applications. Such applications have increased in the degree of complexity and

dynamics in customer requirement. This increase in customer awareness causes a high

degree of competition in business. In such environment, “supply networks” is a trend to

be considered as a strategy and operation beyond the boundaries of the individual firm.

Supply networks increase their importance when business moves to e-commerce where

communication and transaction across different companies are in concern. The

challenges related to improving product quality, customer service and efficient

operation cannot be effectively met by isolated change to specific organisational units,

but instead depend critically on the relationships and interdependencies among different

organisations (or organisational units) [cited by Strader et al, 1998]. The strategies focus

on much closer relationships between suppliers and customers. Companies tend to co-

operate with other companies rather than internalise and control activities and resource

through vertical integration [ION Project, 1999].

2.1 Supply Networks: a Definition

“Supply networks” is a relatively new term evolved from the fields of supply chain

management, channel management, logistic and lean supply/enterprise. These areas are

all linked to operation management and strategy [ION Project, 1999]. Many definitions

given imply supply networks as the co-operation of organisations in order to process the

flow of material and information. These processes ranging from the purchasing activity,

through the operation and out to customers by the way of distribution or service

delivery activity are seen as integrated processes. This approach is close to a

conventional supply chain, which is considered a special kind of supply networks.

4

Unlike supply chain, which is a single route through a total network [ION Project,

1999], supply networks include horizontal relationships, which are links between

companies at the same level. In other respects, a supply chain is seen as an immediate

supply network, which has one tier of customers, and suppliers [Slack et al, 1998] but

supply networks take into account the vertical relationships expanding through several

tiers. However, in supply networks, a non-linear relationship gives a more complex,

albeit holistic and strategic, view of the processes.

Grapher defined supply networks as “vertical interfirm co-operation and disaggregation

of large firm” [cited by ION Project, 1999]. This definition implies the degree of

dependency and relationship among organisations, which form networks. Traditional

economics considers vertical integration as the basic pattern of relationship between

suppliers and consumers. However, integrated business face a risk of becoming locked

into appropriate technologies [cited by Harland, 1996] and this causes a loss of

flexibility when they commit to these technologies. Furthermore, unstable market

condition and changeable customers’ requirement induce modern firms to invest in

flexible manufacturing technologies such as multi-purpose machinery and a versatile

workforce able to adapt to changing conditions [ION Project, 1999].

ION Project defined supply networks as “A number of entities, inter-connected for the

primary purpose of supply of goods and services required by end customers” [1999].

The term “inter-connect” implies that a supply network is not only a group of entities.

Even though their works are of great relevance, they are different. The entities are

connected through transaction of goods or services and no organisation controls the

network. Furthermore, the relations in a supply networks consist of a spectrum of

supply relationships ranging from tight long-term to loose short-term relationships.

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2.2 The Network Perspective

The term “network” refers to two or more organisations involved in the same

transaction. Transaction means that organisations are connected and have long-term or

short-term relationships depending on the quality, quantity, and type of interaction

between the parties. With their relationships, however, organisations are interdependent,

that is, each link must examine the others for its own maintenance because

understanding the maintenance of other links is its dependency upon their performance.

Each link must maintain its performance not only because of its relations with other

links but also because of itself being confront to competitive pressure. Organisations

within the network can occupy different relations so they are not necessarily tied to a

specific tier level.

The network includes supplier and customers and also includes suppliers’ supplier or

suppliers’ a set of suppliers and the same in the customer side. The figure that we use to

describe supply networks is an hourglass shape as illustrated by Figure 2-1.

Figure 2-1: An “Hourglass” supply network

(Reproduced from Parunak and VanderBok, 1998)

The example process in a supply network begins from a manufacturer. On its supply

side or on the lower part of an hourglass, it is supplied by its suppliers or assemblers,

ConsumersDealers

Manufacturer

Assemblers

Suppliers

6

who in turn purchase their own material from their suppliers. On the demand side or on

the upper part of the hourglass, the manufacturer supplies products to its customers or

dealers (distributors), who in turn may have their own customers. The entities directly

connecting to the manufacturer are called ‘first tier’ customer/supplier. On the supply

side, they are supplied by ‘second tier’ supplier. However, some primarily ‘second tier’

supplier may supply the manufacturer directly and the same should happen in the

demand side.

2.3 Activities within Supply Networks

As mentioned above, processes within supply networks range from the purchasing

activity to the distribution or service delivery. Each process occupies different parts of

the supply network as shown in Figure 2-2. However, not only supply flows through

this networks, there is also information and currency.

Figure 2-2: Activities within Supply Networks

(Adapted from Slack et al, 1998)

2.3.1 Channel Management

This topic involves the management of a channel of distribution, which is a group of

individuals and organisations that direct the flow of product from the manufacturer to

First-tier customers

Manufacturer

Second-tier customers

First-tier suppliers

Second-tier suppliers

1. Channel management2. Material management3. Logistics4. Supply chain management

12

4

3

7

customers [Dibb et al, 2001]. Its aim is to provide customer satisfaction, that is, supply

products in the right time, right place, right quality and quantity. Channel management

focuses on the various types of channel structure, which is the number of links or

directions between the manufacturer and the end customers. Moreover it also focuses on

the behaviour within that structure, including power, conflict, leadership, and co-

operation in channel. Typically, the dominant feature of channel relationships was

power relationships and conflict. However, nowadays a comprehensive market

increasing in intensity, product technological complexity and economic pressure has

increased the need for organisations to seek mutually acceptable objective and strategy.

In contrast to coercion and conflict, interdependence, which implies a cooperation and

mutually beneficial relationship, is an appropriated strategy.

2.3.2 Material Management This concept originated from the purchasing function, which contacts with suppliers to

buy material or service and needs to understand the requirement of all processes and

capability of suppliers. Material management includes the function of purchasing,

expediting, inventory management, production planing and control and physical

distribution management by concentrating on material. These various activities take

place in different locations and are managed by different parts, which causes a difficulty

in controlling material flow efficiently. Central to the concept of material management,

it is reducing total cost as well as the acquisition and the management of material.

Moreover, its responsibility to the whole material movement and information flow

concerns quantity and lead time.

2.3.3 Logistics

Logistics refers to the total flow of finished goods downstream from the plant to the

customers. The term logistics is analogous to physical distribution management, which

8

involves the physical transportation of goods and service to customer [Slack et al,

1998]. However, logistics is adopted to extend to include more of the total flow of

materials and information, which is the information flow from customers to suppliers

and a flow of material going in the opposite direction. The concept is to smooth the

flow and make it continuous and also to minimise the delivery time from supplier to

end-user. Logistics coordinates the activities of the traditional distribution function

together with purchasing, material planing, manufacturing, marketing and research

development by concentrating on finished goods. The primary objective of the logistics

is to make a cost-effective response to customer’s service requirement and its focal

point is customer satisfaction.

2.3.4 Supply Chain Management

The term supply chain management was originated in the early 1980s, when Oliver and

Webber discussed the potential benefits of integrating, manufacturing, sale and

distribution [Harland, 1996]. The term is a strategic concept, which includes the long-

term consideration of supply management issues as well as a shorter-term control of

flow through the supply chain. For a long-term consideration, supply chain management

includes developing appropriate relationship types between individual link, i.e. between

supplier and their customer. For the shorter-term control, it includes developing and

understanding the dynamic effect (the Forrester Effect) which plays the role on the

amplification of demand changes that affect upstream operation within the supply chain.

The focus of supply chain management is on satisfying end customers by developing

the internal and external flow of material and information. There are two issues about

customers that is, firstly, customers who have the real currency in the supply chain can

trigger all activities along the whole supply chain. Secondly, customers are satisfied by

the value added into products along the value chain, which is a model of how businesses

receive raw material as input, add value to the raw materials through various processes,

and sell finished products to customers [bettermanagement, 2001]. Each organisation

9

within the supply chain passes on portions of that end-customer’s money to each other

and each retains a margin for the value it has added.

2.3.5 Lean Supply

Lean supply is the other theory related to supply networks. The concept of lean thinking

is to fuse the benefits of standardisation and responsiveness [cited by Holt and Root,

2001] by refining information and product exchange from one process to the next or one

organisation to the next. However, the central idea of lean thinking is to identify and

eliminate waste in order to achieve the objective of improvement in performance

through processes and product by developing, producing, and distributing products in

less time and expense, increase flexibility and customer responsiveness. Lean

production focus on

• Eliminating unnecessary steps

• Aligning all steps of an activity in a continuous flow

• Recombining labor into cross-functional teams dedicated to that activity

• Continually striving for improvement

[ION Project, 1999]

Womack and Jone developed the so-called ‘lean enterprise’, which is a group of

individuals, functions and legally separate but operationally synchronise companies

[cited by ION Project, 1999]. They focus on a value stream, which involve in supplying

goods and services in the maximum value to the customer. This idea is some part of the

notion of a supply network.

Lean supply is developed from a lean theory. It focuses on “lean” in a relationship,

which is more realistic and appropriate method of collaboration than partnership, that

are often dominated by customers. Even though lean concept has the general idea about

10

co-operation similar to the supply network concept, they emphasise the removal of

waste. Furthermore, the main concept of lean supply is the relationship of ‘quasi-firm’,

which gives equal importance to suppliers, and consumers, while all activities of supply

networks focus on providing customer satisfaction.

2.4 Insight supply networks

Most works on supply networks focus on specific industries such as automotive

industry or textiles and clothing [cited by ION Project, 1999]. This is because each

industry has a specific characteristic. For instance, in the automotive industry, we have

long supply networks, comprised of several tiers. One supply network is different from

another network and this divergence affects the nature and shape of networks.

Consequently, various strategies and approaches are discussed under supply networks

theme.

2.4.1 Structure

Supply networks are different in terms of size, shape, and ownership and these factors

affect the performance of the networks. The length of the networks or the number of

tiers affects the interdependence within networks. The longer the network, the more

independent the business, which means that since the components have independent

objectives the chain is more difficult to co-ordinate [Slack et al, 2001]. The breadth of

the network depends on how the organisation manages their suppliers. Single sourcing

and multi sourcing are discussed. Multi sourcing provides an ability to adapt to changes

in the environment through switching because of more contacts. However, Single

sourcing networks would be more rigid and stronger as there would be dense flows of

exchanges within them[cited by Harland, 1996].

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2.4.2 Strategy

Related to the structure of networks, it is important to know the position of the

organisation. This refers to vertical integration, which is the extent to which the

organisation owns the network of which it is a part [Slack et al, 1998]. It means to

decide whether organisation will make a particular component itself or buy it from

supplier i.e. make or buy decision. Backward or upstream vertical integration or

expanding on the supply side allows an organisation to take control of its supplier.

Forward or downstream vertical integration takes an organisation closer to its customer

and allows more freedom for the organisation to make contact directly with its

customer. To take a network perspective, organisation can also choose collaborative

partnership in network and monitor your own and your competitors position in the

network. The other concern is about the competitive dimension. All five-performance

dimensions: quality, speed, dependability, flexibility and cost should be regarded in a

balance [Slack et al, 1998]. In addition, the most important concern is the relevance

within networks. The overall strategy within a network should be perceived more

informally as goal congruence [cited by ION Project, 1999].

2.4.3 Relationships

This topic discusses how organisations co-operate with each other and in which domain.

Vertical integration may make it easy to control activities, but limit the degree of

competition. In relationships, power can be seen as the ability to influence the decision

or action of other [cited by ION Project, 1999]. Power can be defined as knowledge,

resource, technology competence and information. In addition, power may locate in one

or several positions within a supply network, which affects how some companies can

control the network. The power between customer and supplier relates to the control of

activities and resources. Power may not balance in form of interdependence as buyers or

suppliers are more interested in long-term relationships. The other aspect of

12

relationships is trust, which is the key to keep organisations together in network and

have an influence especially in channel management. Trustworthiness in buyer-supplier

relationship is not acting in a purely self-serving manner, accurately disclosing relevant

information when requested, but changing supply specification, standard or cost to take

advantage of other parties and generally acting according to normally accepted ethical

standard [cited by ION Project, 1999].

2.4.4 Dynamics of Supply Networks

Many efforts to manage supply chains have led to frustration because of the dynamics

and complex nature of supply networks, especially in the upstream part. This topic was

demonstrated in the 1960s by Jay Forrester that certain dynamics exist between firms in

supply chains [Slack et al, 1998]. He identified that demand information about timing

and volume of requirements became increasingly distorted further upstream in supply

chain. This cause errors, inaccuracies and volatility. In the sense of networks, dynamics

can also govern network relationship. While individual relationships might endure, the

change to the total system of supplier relationship was rather large. This indicates that

networks are not static but change in dynamic manner as a result of external and

internal factors.

2.5 Summary

A supply network consists of a number of entities, inter-connected for the primary

purpose of supplying goods and services required by end customers. The degree of co-

operation between organisations ranges from loosely short term to tightly long term.

The supply network deals with the integrated processes of the purchasing activity,

through the operation and out to customers by the way of distribution or service

delivery activity. The supply network is a strategy and operation and is applicable in e-

13

commerce. However, the dynamic and complex nature of supply networks causes many

difficulties to predict and control. With this motivation, many efforts are contributed to

the study of supply networks.

2.6 Supply Networks in the experiments

In our experiments, we set a scenario of 3-tier supply networks as an upper part of an

hourglass diagram. Our supply network consists of a producer on the lowest layer,

dealers on the middle and consumers on the upper layer. The producer, who has a

limited stock, supplies products to the dealers and the dealers supply the products to the

consumers. The dealers have to maintain their stock level, fixed cost of operation and

sell price. Along with the products, messages and currency flow in the supply

networks. The diagram of our supply networks is shown below.

Figure 2-3: Our 3-tier Supply Networks

C C C C C

D D D

P

...

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Chapter 3: Modeling and simulation

Model is a substitute for a real system. The informal models we actually use all the time

are the images we carry in our minds. Such images are simplified representations of

complex system and influence how we understand the world and how we take action

[Ford, 1999]. We can construct a model, which represents one system in order to

explore interesting phenomenon or behaviour. When we propose a model we

deliberately eliminate and simplify aspects of the real world [cited by Vasconcelos and

wooldridge, 2001] so as to make it easier to understand. However, with the simplicity of

model, reality with all its complexity should also be observed, its distinct aspects

isolated and classified with respect to their relevance depending on the purpose of the

model[Vasconcelos and wooldridge, 2001].

A simulation is the execution of a model, which consists of analysing the attribute of the

model of the surrounding world. If we regard a model as a static representation of the

real world, a simulation can be seen as a mapping among such model. The dynamics of

the real world can be represented, or simulated, as changes in the static models through

time [Vasconcelos, 2001]. We can analyse a model by using many approaches, some of

which provide theoretical method such as analytical approach, On the other hand, the

simulation approach provides a flexible and practical method. Moreover, computer

simulation model simulates the real system into computers in order to study that system

with much greater complexity, more simultaneous calculations and rapidly. A well-

designed model can be used to conduct experiments and then can describe the system

we are interested in.

In this chapter, we introduce 2 distinct modeling which are employed in business

scenario, which have been seen as dynamics. These two approaches are Equation-based

modeling (EBM) and Agent-based modeling (ABM). They both simulate the system by

constructing a model and execute but different in the form of the model and how it is

15

executed. EBM represents the system as a set of equation whereas ABM represents the

internal behaviour of each individual [Parunak et al, 1999]

3.1 Equation-Based Model

Equation-Based Model is a mathematical model that uses equations to present the

interconnection and dynamics in a system. The model is built by a set of equations,

which expresses the relation among variables, which are interesting factors in the

system.

Conventionally, EBM is mostly used in computational model and simulation of

dynamic system. System that is said dynamics is the system that change over time and

its composition affect each other, such as business system and social system. Most

modeling software in this field is visual modeling environment and also is developed

diagrammatically rather than lines of text command. In this part, we will present the use

of EMB in 2 modeling software .The first one is Vensim simulation which is mostly

used in the field of system dynamics. The latter is Simile which was design as a 'System

Dynamics plus objects' language and was used especially for the ecological,

environmental study.

3.1.1 System Dynamics

System dynamics is a methodology for studying and managing complex feedback

systems, such as one in business and other social systems. It studies how system

changes overtime. This field was originated in the 1960s with the work of Jay W.

Forrester [Ford, 1999].

16

Coyle defined systems dynamic as a method of analysing problems in which time is an

important factor, and which involve the study of how a system can be defended against,

or made to benefit from, the shocks which fall upon it from the outside world [cited by

Ford, 1999].

System dynamics can be applied to study situations in real world. This methodology

does not break the system up into smaller and smaller components but look at system’s

behaviour as a whole. The central concept to system dynamics is to understand how all

the objects in a system interact with one another. The objects and people in a system

interact through "feedback" loops, where a change in one component affects other

components over time and in turn that component affects the original component.

Feedback refers to the situation of cause and effect and loop refer to a closed chain of

that cause and effect [Ford , 1999]. Consequently, one cannot study each component

independently and predict how the system will behave. The study of the whole system

as a feedback system will lead to appropriate results.

3.1.2 Dynamic model Dynamic model is one category of mathematical model that represents how a system

changes over time. It will be used in system dynamics community to observe the

system. The fundamental dynamic patterns of system are growth, decay and

oscillations. The early work was Forrester ’s Industrial Dynamics. It was applied the

concept from feedback control theory. The other best-known work was Urban

Dynamics, which explained the pattern of rapid population growth and subsequent

decline in metropolitan in USA. There still are many works related to system dynamics

that were developed later by using equation-based model. Most works are in the field of

geography and environment.

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For the software developed as dynamic model, it widely uses graphical notation for

representing continuous systems and equations to build the model. A system dynamics

diagram typically contains 4 main types of element: stock, flows, variable or converter,

and influences. Equations are put around the diagram. There is also a diagram of

feedback loop, which is a technique to portray the information feedback in the system.

Stocks are used to represent key variables in the model. They represent where the

accumulation takes place in the system. They can change over time but less rapidly than

other variables in the system, so they are responsible for the “momentum” in the

system.

Flow is used to represent variables that change the system to a new state. They are

actions that directly influence stocks and have the same units of measurement

Converter is another kind of variable that describes the flow. They also can be used to

calculate additional measures of the system performance.

Feedback loop is cause-and-effect relationship between each stock.

Equilibrium point is the point that we use to observe the system. To understand a

dynamics, we should notice the moment of equilibrium of the system at some point of

simulation.

Figure 3-1 : Flow diagram along with the corresponding feedback loop

(Reproduced from Ford, 1999)

Bank balance Interest added

Interest rate

++++

stockFlow

Converter Feedback loop Diagram

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So as to illustrate the theory of EBM, two fascinating software developed on the basis

of this approach are demonstrated as the example. The first one is Simile, graphical

software of the University of Edinburgh with the simulation in environment. The

second one is Vensim, visual software of Ventana Systems, Inc with the simulation in

business process.

3.1.3 SIMILE

Simile is visual simulating modeling software especially for ecological, environmental

and related disciplines, developed by Institute of Ecology and Resource Management,

The University of Edinburgh [IERM, 2001]. It is a declarative modeling environment,

which different from a procedural approach in a conventional (procedural)

programming language, In declarative modeling, we represent a model as a set of facts

that are true about the model and the order in which we present the facts is irrelevant.

The full set of facts defining a model actually constitutes a specification for the model.

In this example model about population dynamics, we are interested in the population of

animal in each age level. The population represents the number of animals in the

population or the population density (number per unit area) .We will study its dynamics

influenced by reproduction rate and mortality rate. The population, which is our key

variable, is represented by 4 compartments, one for each of 4 age classes. The first one

represents the first year group, the second represents the number of animals aged 1-5

inclusive, the next animals 6-15 inclusive, and the fourth for all remaining animals.

The flow of population represented by arrow and bow-tie shows how the population

increases and decreases over time. The flow – in causing the increase in the population

comes from ‘ birth’, which depends on each age ’s reproduction rate. The flow from the

first year group to the second group is ‘aging’ or the animals that grow up to the other

age class. The flow- out causing the decrease in the population comes from ‘death’ ,

19

which depends on each age ’s mortality rate. We call both reproduction rate and

mortality rate converters. Reproduction is assumed to be density dependent .The

reproductive rate per individual, is influenced by total population size (declines as

population size increases), as well as is different for each age class. Note that the first

age class does not reproduce, so there is no a ‘births’ term for it. Mortality is the

mortality rate per individual, m, which is independent of population size but depends on

its age. There is a different m value for each age class.

Figure 3-2: Model of population dynamics (Copied from IERM, 2001)

When we run the model and plot the dynamics of the total population size. The graph

shows the variable called 'pop size', which is the sum of the population sizes for each

age class, changing over time.

20

Figure 3-3: Total population size (Copied by IERM, 2001)

This shows logistic growth, which actually will differ in detail, since the single-valued

parameters we had before are now different for each age class. We are able to inspect

the dynamics for each of the 4 age classes. We see that the stable total population size is

reflected in stable numbers in each of the 4 age classes, with different numbers in each

class reflecting their different population parameters.

Figure 3-4: Population size of 4-age classes (Copied from IERM, 2001)

21

Simile is stated that it can handle most model types such as 'differential equation model'

and 'individual-based model'- sometimes directly, sometimes with some recasting.

However, a model in Simile can combine what would normally be considered distinct

and non-combinable modeling approaches, thus it is possible to have a model that

combines, for example, differential equation, matrix, and individual-based modeling

[Muetzelfeldt and Taylor, 2001]. From the above simulation, implementation with

Simile using EBM is rather straightforward when important factors can be transformed

into equations. Some 'disaggregation' characteristic which is one type of component is

divided into a number of parts can be modeled by separating that component into sub-

component. In addition, there are many works of environmental area employing EBM

and the result is rewarding such as Mono Basin Project [Ford, 1999]. However, in the

field of business, whether EBM is suitable or not will be investigated further. As

mention above, the next example of equation-based simulation software is Vensim and

we will explore it with business scenario.

3.1.4 VenSim Simulation This simulation work is one in DASCh project – Dynamical analysis of Supply Chain –

which explore the dynamical behaviour of manufacturing supply network [Parunak et

al, 1998]. The model is built to explore dynamic supply chains using a set of ordinary

differential equations appearing in Vensim simulation environment.

“ Vensim is visual software to help conceptualise, build, and test system

dynamics models. It was created by Ventana System,Inc. as a high end tool to support

the company’s consulting projects for governments and business “ [Ford, 1999]

22

Vensim software uses a rectangular box and the arrow with bow-tie symbol to

represent stock and flow respectively. The simple example of business process

simulated by Vensim is the flow of material in the DASCH .

Figure 3-5: System Dynamics (ODE) Model (Copied from Parunak et al, 1998)

In the simulation of material flow, we are interested in the level of inventory,

which can map with stocks while flows are material flows that flow from one level to

another. The converters are rates of flows that directly influence the flows. From the

Figure 3-5, the 4 rectangular boxes represent the level of work in process and finished

good inventories of each site. The arrow with bow-tie represents material flow that

flows from one level to another and bow-tie or valve is rate of the flow. The rates

depend on other factors such as order period, order rate, production time and capacity.

These factors influence the flow by the symbol of single arrow. The cloud shape at the

upper-left corner represents external source, which is consumer, and producer for the

lower-right corner.

23

The dynamics of this model can be represented by the following set of ordinary

differential equations (ODE’s)

d(WIP3)/dt = orderRate – min(capacity , WIP3/productionTime)

d(Finished3)/dt = min(capacity , WIP3/productionTime) – A

d(WIP2)/dt = A – min(capacity , WIP2/productionTime)

d(Finished2)/dt = min(capacity , WIP3/productionTime) – B

A = orderRate if Finished3/orderPeriod + capacity > orderRate, otherwise 0;

B = orderRate if Finished2/orderPeriod + capacity > orderRate, otherwise 0;

[Parunak et al, 1998]

OrderRate is the rate of consumer orders to the chain;

ProductionTime is the time needed at site2 or site 3 to turn WIP to finished goods;

Capacity is the amount of WIP that site2 or site 3 can turn into finished goods each step

Figure 3-6 Inventory Oscillation in EBM (Copied from Parunak, 1998)

This model shows the interplay between site capacity and order rate. From the figure, it

demonstrates oscillations, for Demand/Capacity = 220/110 ,when the order rate

exceeds the site capacity.

This work provides satisfied numerical result within the limit of model’s feature. Most

aspects represented in EBM must be able to interpret into rate and level to create

equations. However, in real world, many behaviours are difficult to translate into

24

equations or level-rate concept, especially in business world. Therefore, these

behaviours can not be integrated in the model or with arduousness.

3.2 Agent-Based Model

In this section, we consider another approach to modeling and simulation, the agent-

based modeling. Agent-based modeling uses the approach of agent in order to analyse

the real world instead of using forms of mathematical relationships between variables

representing physical values, which can be measured in reality. ABM introduces

individuals, their behaviour, and their interactions. The main difference between EBM

and ABM is how it analyses the system. EBM will see the system as a whole because of

the dependency between components while ABM will pay attention to the

characteristics of each component, which is called agent.

Agents are independent, autonomous and rational components with an identity and

which interact with an environment [Vasconcelos and wooldridge, 2001]. Agents are

rational components in the sense that they are able to perform deduction or inference

using a representation of aspect of the real world. Moreover, they are autonomous in

their decision-making and their acting in the world, without being controlled by a

central unit. They exhibit intelligent behaviour based on their interaction and

communication with other agents, its environment and its internal reasoning.

Agent-based models are built from a collection of agents, which represent the system,

based on individual’s behaviour. Simulation of the system will explore its dynamic

aspects. A multi–agent simulation is based on the idea that it is possible to represent and

emulate the behaviour of entities which are active in the real world, and that is possible

to represent a phenomenon of the interaction of an assembly of agents with their own

operational autonomy[Ferber, 1999]. Like EBM, ABM performs tasks in complex,

dynamically changing environments. The interest in using ABM, is not only that the

25

collective behaviour of agents can be studied, but also individual’s behaviour, which is

changeable due to sophisticated reasoning. Moreover, ABM considers both quantitative

parameters (digital parameters) and qualitative parameters (individual behaviour).

Many motivations for employing ABM in the business scenario, especially in supply

networks bring about some pioneer works. We will explore two works in the field of

supply network. The first one is Simulation of Order fulfillment in divergent assembly

supply chains [Strader et al, 1998]. This project is interesting because it is implemented

on SWARM, which is new software supporting the development of multi-agent

simulation models. Nevertheless, it is claimed that this work provides the results on

inventory levels and cycle times but it provides no dynamical analysis [Parunak et al,

1999]. The second one is the Dasch project, which emphasises the dynamics behaviour

of supply networks, and uses this assumption in building a model.

3.2.1 Simulation of Order Fulfillment in Divergent Assembly Supply

Chains

This project employs multi-agent simulation to implement a supply chain scenario. The

management of supply chains is a difficult task involving coordination and decision-

making across organisational boundaries. Computational modeling using multi-agent

simulation are appropriate command and control [Strader et al, 1998]. The model is

used to study the impact of information sharing on order fulfillment in divergent

assembly supply chains. In such supply chain networks (SCN), a small number of

suppliers provide materials and sub-components, which will be assembled at factory or

at a distribution site depending on how complex the process is. Two main goals to

achieve for this type of supply chains are reducing the lead-time of the assembly-to-

order process, and managing the inventory and purchasing for the assembly. Figure 3-7

shows the interaction and message passing among agents.

26

Figure 3-7: SCN Agent Interactions in the Swarm Implementation

(Copied from Strader et al, 1998)

An entity ScnESwarm A receives an order from its customer ScnESwarm C. The order

flows to the order management agent (OrdM) who gets information from InvM, PrdP,

CapP and assigns a due date to the customer. The SCN management agent (ScnM) uses

material planing from MatP who obtains build plans from the production-planing

agent(PrdP) in order to choose suppliers in allocating material sources. Most activities

of these agents are incorporating the decision making, and information and material

processing.

They simulate this model by using 15 business entities and 5 tiers ,dealing with

manufacturing and assembly processes. The aim of the experiment is to evaluate the

order fulfillment process (OFP) whose main task is to estimate the demand for next

27

period and the due dates of incoming order. To achieve this goal, they use various

demand product policies such as make-to-order, make-to-stock, assembly-to-order so as

to predict the demand and different kind of information-sharing strategies.

This project gives two conclusions. Firstly, agent-modeling can be used in practice to

simulate order fulfillment in supply chains and be a tool to identify the impact of

various decision-making scenarios on performance measures such as cycle time and

inventory levels. Secondly, from the simulation of divergent assembly supply chains,

inventory costs are reduced while order cycle times remain relatively stable, which

means that information technology is important for supporting the order fulfillment

process in SCN.

We can observe that it is rather difficult to conduct such experiment, which use

decision-making approach by using EBM. In addition, the hierarchy of business entities

is not sensible for EBM.

3.2.2 The DASCh Project

The other work dealing with ABM for supply networks is the DASCh project which

employs ABM model system dynamics [Parunak et al, 1999]. With the same scenario as

EBM’s simulation, DASCh uses ABM, representing the various components of the

supply chain by software agents that emulate their actual behaviours.

28

Figure 3-8: The DASCh Supply Chain (Copied from Parunak et al, 1998)

The experiments involve a linear supply chain with four company agents. As seen in the

Figure 3-8 of the DASCh model, there are three kinds of agents, the company agent, the

PPIC agent and shipping agent. The company agent represents the different firms in the

supply networks. They consume material from their supplier (external component) and

transform it into output before sending it to its consumers (external component). PPIC

agents model the production planning and inventory control algorithms, supporting a

simple material planning model (MRP). Company agents use PPIC agents to determine

what input to order from their supplier, based on the orders from their customer.

Shipping agents move both information and material between company agents.

The simulation starts from orders and shipment from and to consumer whose demand

varied with noise. The Sites’ PPIC has to forecast the incoming orders for its product

and predict its future inventory and outgoing order for material. The suppliers with

make-to-order approach will supply material with some delay depending on when they

receive orders.

The results of this experiment emphasise variation in supply chain. It shows the

behaviour of amplification, correlation, persistence, and generation of variation in the

29

orders and inventory levels in the system. These phenomena introduce distortion in

order stream, which obscure the suppliers’ view (upstream) of the top-level consumer’s

demand.

According to DASCh, they state that some behaviours are difficult to translate into a

consistent rate-and-level formalism such as PPIC algorithms. A significant advantage of

using ABM in commercial application such as supply networks is shown in two ways.

First of all, each agent which represents firms or division can maintain internal

information about their operation. Agent’s internal behaviour is not required to be

visible to the rest of the system. Secondly, each agent corresponds one-to-one with the

individuals (e.g., firms or division of firms) in the system being model, so their

behaviour can be constructed to imitate the real behaviour. Consequently, ABM is

suited to domains, whose unit is the individual such as business domain.

30

Chapter 4: Experiments and Results

In this chapter, we present the experiments carried out in the two 2 approaches for

modeling. One is in Simile, our equation-based model, and the other is our agent-based

model. These experiments will be set up on a supply network scenario, which is

suggested as dynamic system. We will use three kinds of agents: producer, dealer and

consumer. A producer supplies products to dealers and the dealers supply the products

to consumers. Money circulates within the system as revenue and cost.

We conduct 3 experiments with both EBM and ABM in order to compare and contrast

the results. We create an equation-based model and an agent-based model for the first

experiment and adjust both models a little for the second and the third experiments. The

first experiment focuses on the profit of dealers. The second one focuses on the average

sell price and the third one focuses on the profit of dealers when there is the impact of

the other type of dealers.

In our first experiment we shall analyse the system by paying attention to a profit of

dealers when other factors are set in different way. All equations used in models are

based on simple principles of business. They are used on the grounds of plausibility for

business and suitability for our laboratory.

4.1 The first experiment

4.1.1 An equation-based model for supply networks As we have known that the early simulation work in system dynamics employs

equation-based model, we also will explore the dynamics of supply networks by

constructing and experimenting with an EBM by using Simile.

31

Figure 4-1: EBM for Supply Networks : Experiment 1

The Figure 4-1 shows the model of 3-tier supply networks comprising of 3 populations

of components: consumers, dealers and a producer. The right part shows money flowing

from the consumers (upper) to the producer (lower) while the left part shows products

flowing from the producer (lower) to the consumers (upper).

The lower-left rectangular box labeled “PStock” represents a producer’s stock at whose

zero level the system will end. The bow tie labeled “Ship1” represents the condition and

rate of products’ quantity delivered from the producer to the dealers. It is set to check

when a dealers’ stock is empty, the products will be delivered to the dealers’ stock

(Dstock1) at certain amount. While the products flow from the producer to the dealers,

dealers’ money flows to the producer as its cost. There are 2 kinds of costs that are “C1”

: variable cost and “OvCost” : fixed cost. Price1 is a product’s sell price, which varies

between each lot of products delivered from the producer.

32

VariableCost (c1) = Ship1*1/PStock ; Ship1 is a delivered amount of products per

lot

1/PStock is assumed as a product’s price,

whereas PStock is a number of producer’s

stock at the delivery time

Product’s price is determined based on the assumption that the more a number of

products the cheaper the product’s price.

FixedCost (OvCost) = x ; x is a constant number

SellPrice ( Price1) = [ Margin * UCost1 ] + UCost1 ; Margin is a constant number

UCost1 is a unit cost calculated

by C1/ Ship1

The upper-left rectangular labeled “CStock” represents a consumers’ stock, which is

filled every tick (one time interval) if the dealers’ stock is not empty, at the rate of

“Del1” which is set to 1 item per tick. At the same time money from consumers’ budget

: “CBudget” flows to the dealers at the rate of “Rev1” which is the sell price for that

product. “DProfit” is dealers’ profit calculated from revenue (Rev1) deducted by cost

(C1 + OvCost). Revenue flows into the dealers’ profit every time the dealers sell their

products whereas cost flows out at the time of the dealers’ stock delivered. Finally, we

will have the model’s equation as follow

Profit =[QuantitySold *((Margin * 1/ProducerStock) + 1/ProducerStock)] –

[(LotQuantity * 1/ProducerStock ) + FixedCost ]

When the model is executed, it runs step by step following the condition and equations

we have set. We count one step of execution as “tick”. From this model, we are

33

interested in the dealer’s profit and will investigate how it varies over time and compare

the dealers’ profit in different environment. With other factors constant, we change

“ProducerStock” variable, which controls the end of the system. We start from

“ProducerStock” 200 in the first simulation and add to 500 in the second simulation.

After that we will take turn to change fixed cost and then margin. The first set of graphs

is created as follow.

ProducerStock = 200

Margin = 1.5

Fixed cost = 0.025

Ship1 = 5

Figure 4-2: Simile Graphs1 : Experiment 1

34

The Figure 4-2 illustrates the growth of profit, revenue, and cost against time while X-

axis is time and Y-axis is money. The upper graphs show cost against time and revenue

against time from left to right. The graph grows in exponential pattern but at different

rate following the equations. For the lower-left graph, it demonstrates the dealers’

profit, which decrease below zero and then increase in exponential pattern. The lower-

right table is the data of profit and the highlight point is the break-event point, which is

at tick 8, or the product is sold 8 items. The profit graph at this point grows above zero.

This means that firms begin gaining profit after 8 ticks and have most profit at value

11.05. We can explain that, in the beginning phase, cost is higher than revenue and it

causes profit being under zero level. When time passes, revenue, which increases in

exponential rate will get higher than cost, which increases in slower exponential rate.

The break-event point that makes revenue higher than cost or makes profit above zero

depends on 3 variables. The first variable is a producer’s stock, which affects variable

cost. The second is fixed cost, which affects total cost and the third is margin, which

affects revenue .

In the second simulation, we run at “ProducerStock” = 500. The graphs show the same

pattern as that in the first time but in a different scale. The break-event point is at tick

18 and the dealers gain most profit at value 13.22. We can see that even though the

products in the system are increased, it does not affect the system significantly.

The other relevant factors affecting profit are margin and fixed cost. Next we will

simulate our model by changing fixed cost and margin respectively. The second set of

graphs shows the result as follow

ProducerStock = 500

Margin = 1.5

Fixed Cost = 0.05

Ship1 = 5

35

Figure 4-3: Simile Graphs2 : Experiment 1

All graphs exhibit similar growth patterns but the cost graph grows sharply. This

behaviour causes an increase in the break-event point obviously. From the data of the

profit on the right, it indicates the break-event point at tick 217 displaying the profit

0.008. At the end of the system, the profit is at 10.72 comparing to 13.22 of the previous

simulation, which is 18.91 % decrease. We can observe that fixed cost does not only

affect the profit but also influence the break-event point.

For the third set of graphs, we set margin at 3 and the other variables are as follow.

ProducerStock = 500

Margin = 3

FixedCost = 0.05

Ship1 = 5

36

Figure 4-4: Simile Graphs3 : Experiment1

The graphs illustrate that the profit increases up to 23.27 and the break-event point is at

tick 22.

From this EBM experiment, we can conclude that firms lost at the beginning phase of

investment and after that they will gain profit. There are at least 3 factors affecting how

long firms will gain profit and how much profit they can earn. The first is a quantity of

products supplied in the system. The more the products, the longer the firms can gain

profit. The second factor is fixed cost, which the firms should maintain at low price, and

this factor affects both the profit and the break-event point significantly. The third factor

is margin, which is relevant to fixed cost. If the firms have high fixed cost, they may set

margin at high level to gain an appropriate profit but they have to beware of sell price’s

competition.

4.1.2 An agent-based model for supply networks

As for an agent-based model, we use Sicstus Prolog in order to develop programs that

represent different kind of agents. Each kind can be replicated to any number of

populations. A number of agents communicate by sending messages to one another.

37

Consequently, two main issues are raised in building this model. Firstly, there should be

the amount of identical agents running in the model. In this supply networks scenario,

we need at least 3 kinds of agents: consumers, dealers, and a producer, each of which

can reproduce any number of agents to observe their dependent behaviour. Secondly,

the amount of these agents should run in parallel because parallelism in the execution of

simulation may make substantial performance improvement possible. Faster simulations

will expand the kinds of problems to which simulation can be applied. The ability to

speed up makes interactive, faster than real time [Unger, 1988].

‘Experiment and Lpad’, a tool that supports the replication of agents, and the automatic

upload and execution of a number of agents [Vasconcelos , 2000], is used as a platform

for this model. In view of the fact that prolog can be used as meta-programming which

is a program that generate and run other programs (in the same language), ‘Experiment’

exploits this technique in order to obtain different version of the models. Each type of

agent, which is a prolog program, is replicated as the given numbers and is linked to

other types of agent. By this methodology, our model allows for experiments to be

carried with minimal effort when changing parameters.

Agents run in parallel fashion and send messages to communicate with each other using

the tuple space. This mechanism is introduced in Lpad. As shown in Figure 4-5

Figure 4-5: ABM Lpad Platform(Reproduced from Vasconcelos , 2000)

Ag2

Ag1 Sv Agn

S1 S2 Sn LPad

...

38

When Lpad starts, it automatically starts up a Linda server process, Sv and Linda

client processes AG1,AG2..AGn , one for each S1,S2..Sn representing the prolog codes

for each agent. For each specification Si, Lpad automatically synthesises a self-

contained, executable SICStus Prolog program, fully compatible with the server process

set initially.

For each AGi, it can communicate to each other via Linda server process, Sv. Server

process manage the tuple space where the message can exchange. When agents need to

communicate to others, they just send the message to server .The message should have

its address (sender’s identity) and destination (receiver’s identity). Agents can receive

the message in the same way by checking within the tuple whether there are any

messages whose destination match their identity.

The model consists of three tiers in supply networks. The lowest layer is a producer,

which has a limited stock to supply to the middle layer, which are the dealers. The

dealers communicate with both the producer to order products, and consumers to supply

their order. The number of dealers and consumers varies from experiment to

experiment. Each dealer is linked to a number of consumers.

Figure 4-6: Supply Networks for ABM

A key variable used to measure the performance of the system is the profit of dealers

which is calculated from revenue received from consumers deducted by the cost paid to

C C C C C

D D D

P

...

39

a producer. We will explore the system by setting different factors that are expected to

affect the dealers’ profit. These variables are as follow

1. The number of consumers, dealers and the number of consumers linked to each

dealer

2. Quantity of product in producer stock

3. Margin and fixed cost

When we start the model, Agents will communicate with one another in order to

perform the following action

Consumer Dealer Producer

Requesting Request for item to

Dealer

Request for stock to

Producer

Offering Offer item to

Consumer

Offer stock to

Dealer

Confirming Confirm the offer to

Dealer

Confirm and deliver

product to Consumer

Refusing Refuse the offer to

Dealer

Refuse delivering

product to Consumer

due to temporary out

of stock

Consumers will send request to all relevant dealers in order to ask for the number of

products which is set to 1 item per order. In order to supply the products to the

consumers, the dealers usually maintain their stock’s level if the stock is empty, they

will sent the request to a producer to ask for the products. When the products are

delivered to the dealers, they have to pay money as their cost to the producer. At this

point, the sell price of this lot of product is determined. When the dealers receive

40

requests from consumers, they will send offer message with the sell price. The

consumers collect all messages from the dealers and choose the dealer that provide the

lowest price and send a message to confirm the order. For the other dealers that offer

higher price, they will receive a “refuse” message. The dealers wait for either a “refuse”

or a “confirm” message. For a confirm message, the dealers will check their stock

again, if they have enough stock they will reply “order-processed” message to confirm

the order. In case that they run out of stock, they will send “lot_out” to inform the

consumers that they have no stock for this price. Simulation will run until the producer

runs out of stock and broadcasts the messages “no_product” to all dealers. Each dealer

will die when it runs out of stock and no more stock supplied from the producer. The

dealers will broadcast the “out_of_stock” message to relevant consumers and when all

dealers die, finally, the consumers will die. The protocol for a message-passing is as

follow;

Consumers Vs Dealers

Consumers request product: “Ask_Product(quantity)”

Dealers offer the request: “Offer_Product(quantity , price)”

Consumers collect all message and compare price “Confirm” or “Refuse”

Dealers check their stock: “Order_Processed” or “ Lot_out”

Dealers broadcast a message when they die “No product”

Dealers Vs A Producer

Dealers request stock “Ask_Stock(quantity)”

A Producer offer stock “Offer_Stock( quantity, price)”

Dealers receive stock “Stock_Received”

A Producer broadcast message when it die “Out_of_stock”

41

Figure 4-7: A message-passing Diagram

The formulas used to calculate all prices are the same as in EBM, that is

VariableCost (c1) = Ship1*1/PStock ; Ship1 is a delivered amount of products per

lot

1/PStock is assumed as a product’s price,

whereas PStock is a number of producer’s

stock at the delivery time

FixedCost (OvCost) = x ; x is a constant number

SellPrice ( Price1) = [ Margin * UCost1 ] + UCost1 ; Margin is a constant number

UCost1 is a unit cost calculated

by C1/ Ship1

Profit =[QuantitySold *((Margin * 1/ProducerStock) + 1/ProducerStock)] –

[(LotQuantity * 1/ProducerStock ) + FixedCost ]

Consumer

Ask_Product

Offer_Product

Confirm / Refuse

Ask_Stock

Offer_Stock

Stock_ReceivedNo_Product

Out_of_Stock

Order_Processed / Lot_outProducerDealer

42

The advantage of ABM is we can explore individual’s behaviour. Three kinds of agents

– that are consumers, dealers, a producer – can have any number of their population and

for each population we can consider their behaviours separately.

For this first experiment, we are interested in the dealers’ profit. How it behaves over

time and which factors affect this behaviour are what we will investigate further. Profit

occurs when dealers communicate with consumers and a producer and is collected for

every loop of a dealer execution. Here, loop means the processes of the dealers ranging

from asking for stock from the producer and its processes corresponding to receiving

stock, and offering the consumers’ requests and its relevant processes. We compare the

growth of profit in various environments in which factors are set differently. First, we

will observe profit when producer’s stock is different. The reason we are interested in

this factor is that it is an external variable that firms or dealers can not control.

The first two graphs show the profit of dealers in the pattern of 20 consumers, 4

dealers, 1 producer and each consumer interacting with 2 dealers. Variables are set as

follow

ProducerStock = 200 for the first graph and 500 for the second one

Margin = 1.5

FixedCost = 0.025

43

Figure 4-8: ABM Graph1 : Experiment 1

44

Comparing these two graphs, we can point out some aspects. Each dealer behaves in a

similar manner, that is, it starts losing for some time and then it starts gaining profit.

When time passes, the difference from bottom to peak is higher. The graphs exhibit

oscillation in a curvilinear trend. They decline at the beginning phase in exponential

fashion and then they increase in exponential rate as well. The oscillation pattern

occurs because the dealers spend money to maintain their stock before they sell

products and this happens in cycle. The factor of “ProducerStock” shows us that if the

producer can provide a huge number of products in system, the dealers take a long time

to recover their losses. The lower graph, which “ProducerStock” = 500 shows the

break-event point, where profit is above zero, is at the half way of the system. In

addition, the interesting point we observe from this model is at the last phase, even

though all dealers’ profit increase in overall, one dealer’s profit decreases significantly.

It suggests a variation of noise from other dealers in this model.

We run the second simulation with the same variables but with a different number of

agents in order to discover any effects from this factor. The variables are

ProducerStock = 500

Margin = 1.5

FixedCost = 0.025

We will change our environment by running this simulation in the pattern of 100

consumers, 10 dealers, 1 producer and each consumer interacting with 5 dealers. The

graph displays the similar pattern as the previous ones but the trend is straighter.

Moreover, all dealers encounter lost almost system’s life cycle. It suggests the effect

from one dealer to the others and also shows a high competition in the system.

The last graph we will present the effect from the rest variables, margin and fixed cost.

We set environment as four dealers in the pattern of 20 consumers, 4 dealers, 1

producer and each consumer interacting with 2 dealers. The variables are

45

ProducerStock = 200

Margin = 2

FixedCost = 0.05

Figure 4-9: ABM Graph2 : Experiment 1

We make the value of margin and fixed cost larger to be 2 and 0.05. Unpredictably, the

graph starts a sharp downward movement and take long time before turning to upward.

From this phenomenon, we can conclude that fixed cost is more sensitive than margin.

Increase in fixed cost even in a small number originates a serious lost.

46

4.1.3 Compare and contrast the experimental results

The purpose of performing the experiment from two models – EBM and ABM is to

compare and contrast the experiments’ result from each simulation. We have built these

two models by using the same scenario that is their main formulas for calculating each

value are comparable. The difference of these two models is the EBM models each

component as a collection of population whereas the ABM models its components as

individual. In the EBM, one rectangle box represents a group of one kind of its

component, consequently we can not see what happen inside that box. In ABM, one

kind of agent can reproduce any number of its agents and within the same kind of

agents, they can act in order to respond to the others. For the Figure 4-10, the left graph

is from the EBM and the other is from the ABM.

47

Figure 4-10: Comparison for EBM and ABM graphs : Experiment 1

ProducerStock = 500

Margin = 1.5

FixedCost = 0.025

These two graphs display the same pattern but they are different in some ways. We use

a producer’s stock to control when the system should finish. With the same producer’s

stock, in ABM, the model takes shorter time interval (tick) to run the simulation

because in ABM, there are many dealers to consume and distribute products at one tick.

Moreover each dealer has to compete with each other to sell the products and this

phenomenon affect the break-event point. In EBM, dealers ‘ profit is below zero for a

few ticks after the system starts while in ABM, it takes much longer time. The most

important is that they represent values in different scale. It means even though we use

the same fixed cost rate, the value of profit at one tick is different. In addition, as we

have mentioned about the last phase in ABM, even though in general, the dealers’ profit

increase, one dealer ’s profit decrease significantly. This event does not show in EBM,

which see all dealers as a collection.

48

4.2 The second experiment

In our second experiment, we are interested in the sale price of products. Price is an

important factor in the company’s profit since it affects the profit equation in several

ways. It directly influences the equation because it is a major component. In the profit

equation, price is a key variable, which directly generates total revenue. Moreover, it

has an indirect impact because it can be a major determinant of the quantities sold [Dibb

et al, 2001]. Price competition is one strategy in business, which emphasises price as a

means to beat the price of competitors. However, in order to achieve a price competition

strategy, companies should maintain low cost also because cost is the other factor,

which directly affects profits. In this experiment, we aim to investigate the behaviour of

an average price. Moreover it is interesting to find out the average price that makes a

dealer gain profit the most.

We will investigate two average prices. The first is the average price that is the mean of

prices, which all dealers sell their products. It is calculated by the summation of all

dealers’ average price at each tick divided by a number of ticks. The second is the

average price that is the mean of prices, which each dealer sells its products. It is the

average price of each dealer, which is calculated by the summation of price that a dealer

sell its product at each tick divided by a number of tick. We investigate the first average

price in order to observe the behaviour of price over time and the second average price

in order to find out the price that the most profitable dealer sell its product in

competitive market. Similar to the first experiment, we will conduct the experiment

both in ABM and EBM and compare the results.

4.2.1 An equation-based model for supply networks

We use the same model as that in the first experiment but use different tools to collect

data.

49

For the first average price, we need the data of all dealers’ average price at each tick,

which can be collected by variable ‘Price1’ shown in Figure 4-11. Variable ‘price1’

represents the average prices of all dealers because of the feature of EBM that considers

its component as a collection. Consequently, a rectangular box of dealer ‘Dprofit’

represents a population of all dealers in the system. The model is run by using

parameters relevant to the previous experiment as the following values.

ProducerStock = 300

MarginOfPrice = 1.5

FixedCost = 0.025

LotQuantity=5

Figure 4-11: EBM for Supply Networks : Experiment 2

Tic k

The data of price every tick for calculating average price

Collect the value of price from this variable

50

From the data, price repeats its value 5 times before changing to a new value because

‘LotQuantity’, which is the products’ quantity per lot that a producer send to its dealers

is set at 5 items. Products from the same lot have the same price and the product is sold

1 item per tick therefore the product is sold at the same price 5 times. The result is

shown as below.

Figure 4-12: Average price from EBM : Experiment 2

The graph shows the average price for every 5 ticks because the products in the same lot

(5 items) that have the same cost are sold at the same price. In EBM, a price at one tick

represents the average price of a dealer’s population. From the graph, the price

increases in a curvilinear trend as well as profit, suggesting that the growth has been

exponential over time. Even more we can observe that the price increases sharply when

it gets closer to the last tick because of the inevitably high cost when the producer

cannot provide the products (according to our assumption that the resources will be

more expensive when time passes or there is the scarcity of products).

51

The second average price is the mean of price that each dealer sells its product over

time. EBM does not provide the price value of each dealer because of the approach of

the modeling so we calculate the average price of all dealers instead. From the data used

for plotting the above graph, we sum it and divided by a number of ticks to obtain the

average price. The average price is 0.0622294421, which is the price at tick 40.

Incoming question is whether average price corresponds to the quantity of producer’s

stock. To explore it, we run the other 2 simulations with the same parameters but

replace the value of ‘ProducerStock’ with 200 and 500. The results indicate that the

average price of small number of product in producer’s stock system is lower than the

bigger one. The average prices are 0.08165 for 200 of the products and 0.04182 in 500

of the products.

4.2.2 An agent-based model for supply networks As we have known, in ABM we can see the behaviour of dealers individually. Each

dealer communicates with consumers by sending an offer of products’ quantity and

price when the consumers request. We collect the price when each dealer send the offer

to its consumer and use this price to calculate the average price. Similar to EBM, we

will investigate 2 average prices. The first is the average price that is the mean of prices,

which all dealers sell their products. The second is the average price that is the mean of

prices, which each dealer sells its products. The model consists of 8 dealers with 20

consumers and 1 consumer interacting with 4 dealers with these variables.

ProducerStock = 300

MarginOfPrice = 1.5

FixedCost = 0.025

LotQuantity=5

For the first average price, in order to obtain the value at each tick, we sum the price

selling by 8 dealers at each tick and divided by the number of dealers. The average price

52

represents the mean of the price every dealer sell its items at each time. The graph

shows the average price changing every tick. We can observe that price increases in

exponential rate and grows sharply when it grows closer the last tick. The shape is

nearly the same as that in EBM but in a different scale, which comes from the basis of

the model.

Figure 4-13: Average price from ABM : Experiment 2

For the second average price, in order to obtain the value of each dealer, we sum the

price that a dealer sells its product over time and divided by the number of ticks. The

average price represents the mean of the price that the dealer sells its items over time.

The graph illustrates the average price ranging from the dealer that profited less (1) to

dealer that profited the most (8).

53

Figure 4-14: Average price of each dealer from ABM : Experiment 2

From the graph, we learn that the dealer that sells products at an expensive price will

not profit the most (Dealer 6). However, the dealer that sells in the same average price

(Dealer 2 and 5) profit differently. We can conclude that the average price is not a good

value using as a standard price because it does not exactly correspond to profit.

4.2.3 Compare and contrast the experimental results

In this experiment, we aim to investigate two average prices. The first is the average

price that is the mean of prices that all dealers sell their products. It is calculated by the

summation of all dealers’ average price at each tick divided by a number of ticks. The

second is the average price that is the mean of prices that each dealer sells its products.

It is the average price of each dealer, which is calculated by the summation of price the

54

dealer sell its product at each tick divided by a number of ticks. The results of the first

average price in EBM and ABM are as the graphs below.

Figure 4-15: Comparison for EBM and ABM graphs : Experiment 2

The left graph is in EBM whereas the right graph is in ABM. These two graphs display

the same pattern but in a different scale. Here, the different scale focuses on the x-axis,

which is 300 ticks in EBM and only 21 ticks in ABM. Similar to the first experiment’s

result, in ABM, there are many dealers consuming and distributing products at one tick

whereas in EBM, product is sold 1 item per one tick. The graphs also indicate that the

average price in ABM is lower than that in EBM because of the same reason. We can

conclude that, in real world, if we will use a simulation approach in one company in

order to simulate the business scenario that focuses on our company and there are

impacts from other companies, EBM may not give the appropriate or could not simulate

some situation. This is because in business, we concentrate on the organisation

individually rather than a group of organisations. Moreover for the average price that is

the mean of prices, which each dealer sells its products, we can not collect that

individual value in EBM because the same kind of component is seen as a collection,

which is the approach of EBM.

55

4.3 The third experiment

In our last experiment, we will investigate profit again but in a different way. We adjust

the system a little by including one more breed of dealers. Here we define ‘Breed’ as a

different type of the same agent. Dealers having different breed means the dealers are

set to have different variables such as a different margin. Therefore, we can compare

two different types of dealers in one system. In our scenario, we maintain a stock by

filling our stock when it reaches a critical level. How many units per lot the dealers

order from a producer is a variable that a manager has to decide. If the dealers order

products in a huge number, they can buy the products at a lower cost since we have the

assumption that the resources will be more expensive when time passes or there is the

scarcity of products. Moreover, the cost occurred in purchasing such as transportation

cost incurs less frequently. However, a major disadvantage is that we would have to

pay a large amount of money each time, which means losing interest if the money is in a

bank. The other cost is the cost of storing large amounts of product, which would be

expensive.

We use the same scenario as that in the first experiment but add one more kind of

dealers in our model. They have the same variables except for the quantity they ask for

a producer at one time and fixed cost. We will explore the behaviour of a profit as well

as the balance of ‘order quantity’ and ‘cost’ in this experiment.

4.3.1 An equation-based model for supply networks In Simile, we include one more set of dealers and a converter used for deciding from

which dealers consumers should buy products depending on the cheaper price that the

dealers provide.

56

Figure 4-16: EBM for Supply Networks : Experiment 3

The experiment starts at Producer’s stock 100 with only one different variable, ‘order

quantity’ and the other variables are as below.

Breed1 Breed2

OrderQuantity 5 3

Margin 1.5 1.5

FixedCost 0.025 0.025

57

Figure 4-17: EBM Graph1 :Experiment 3

The graphs above represent the profit of each dealer against time where time is the x-

axis. The left graph is the breed1 dealers’ profit with order quantity 5 whereas the right

one is the profit of dealers with order quantity 3. The graphs indicate that, with the

same cost and margin, the dealers who maintain a large stock have an opportunity to

gain more profit.

As we have mentioned about costs occurring in the large stock, we adjust one more

variable, FixedCost, in order to see how it relates to profit. Using our model, we tried

many values of fixed cost until at one value the breed2 dealer can gain more profit than

the breed1 dealer. This suggests that suitable order quantity can make the dealers gain

profit and the relevant cost should be in concern. It can be illustrated by the graphs

below.

Breed1 Breed2

OrderQuantity 5 3

Margin 0.5 1.5

FixedCost 0.025 0.025

ProducerStock = 100

58

Figure 4-18: EBM Graph2 : Experiment 3

The left graph is the profit of the dealers who order 5 items each time with fixed cost

0.5 whereas the other dealers order 3 item with fixed cost 0.025. The graph of breed2

reaches profit at 2.455 while breed1’s ends at 0.19.

4.3.2 An agent-based model for supply networks In our ABM, ‘Experiment and Lpad’ tool provides a facility for creating a new breed of

the same agent. We create 2 breeds of dealers, each of which contains different

variables. The environment of this experiment is set at 20 consumers with 4 dealers, 2

of which are the same breed and 1 consumer interacting with 4 dealers with only one

different variable which is ‘order quantity’

Breed1 Breed2

OrderQuantity 5 3

Margin 1.5 1.5

FixedCost 0.025 0.025

ProducerStock = 100

59

Figure 4-19: ABM Graph1 : Experiment 3

Figure 4-19 is the graph of 4 dealers’ profit. 2 dealers of bread 1 can gain more profit

than the others as the same result from EBM. Both breeds start selling their products

and gain profit in similar way at first. After that the difference between profits is more

significant when either time passes or resource ’s price increases.

In ABM, we also try to increase fixed cost of the breed1 dealers and the result indicates

the same. When we increase higher and higher fixed cost, the breed1 dealers gain less

profit while the breed2 dealers gain more profit and at some point the breed2 dealers

gain more profit than the breed1 dealers.

Ticks

Profit

Dealer1(Breed1)

Dealer3(Breed2)

Dealer2(Breed1)

Dealer4(Breed2)

60

4.3.3 Compare and contrast the experimental results

Figure 4-20: Comparison for EBM and ABM graphs : Experiment 3

In this experiment, we aim to investigate the behaviour of profit of two kinds of

dealer.Both models provide the method of creating 2 kinds of dealers. Moreover, we

can observe the fluctuation of profit both in EBM and ABM. The dealers have to pay

their money to buy products to fill their stock and this process causes the decrease in

their profit. The profit will increase when the dealers sell their products. The step-by-

step activity, which is the dealers filling their stock and then selling their product is

implemented in ABM. In EBM, we set this situation as the condition in equations. The

flow from a producer to the dealers is set to check when the dealers’ stock is empty, the

products will be delivered to the dealer’s stock at certain amount. While the products

flow from the producer to the dealers, dealers’ money flows to the producer as its cost.

Even though EBM can support the step-by-step activity in this case, there is an unusual

result at the start point. In EBM, at the beginning, both kinds of dealers have the empty

stock and they ask for products from the producer and the producer sell products to

them at the same time, which is opposite to the real situation. Actually, the producer can

sell products to only one dealer at one time, even though it can contact with many

dealers at that time. We can conclude that ABM is more flexible to implement a step-

by-step activity than EBM and provides more realistic.

61

Chapter 5: Conclusions and Future Work 5.1 Conclusions

In our work, we simulated the supply network scenario, a dynamic system, and used it

with Equation-based modeling (EBM) and Agent based modeling (ABM). Both EBM

and ABM are widely used in social, biology and computer science. Especially in system

dynamics, EBM is employed since this theory has originated. However, there is much

effort and motivation for employing ABM in many dynamic works such as in the

business field. In this chapter we will summarise our two approaches of modeling, then

compare and contrast both models as well as give a conclusion of how suitably these

models are employed within business scenario.

Both EBM and ABM are simulation approaches in order to investigate the dynamics of

system. In both approaches, a model representing the system will be constructed and be

executed experimentally. They are like laboratories, in which researchers can set up,

adjust and control the condition in order to observe the behaviour of their model, which

represents the system. In business, not only forecast values from conventional formula

can be calculated, but some interesting behaviours may emerge unpredictably.

The two distinct approaches of these two models are the fundamental relationships

among entities that they model and the level at which they focus their attention

[Parunak et al, 1999].

• EBM is conducted by a set of equations that represent the relationship among

variables. The result can be observed from the evolution of the variables over time.

Unlike EBM, ABM is conducted by behaviours through which individual

components (agent) interact with one another. One agent ’s behaviour may depend

on other agents’ but does not directly access the representation of those individuals’

behaviours [Parunak et al, 1999].

62

• EBM’s key components are grouped as a collection, whose action affects the value

of variables, which are related to form the equations. Furthermore, such variables

are the variables of the whole system or they are the system level variables. In

contrast, key components in ABM are represented as individuals so variables are

specific for each agent.

The following issues are the observation about both models for our supply network

scenario

• Most software supporting EBM feature a diagram-based language, whose users can

create a model by building a diagram so it is easy to construct the model even

though they are not programmers.

• EBM has a graphic representation. Most results are rather straightforward and easy

to understand because both graph and data represent the system. However, whether

the result is reliable depends on how closely to the real function or situation the

model is constructed.

• ABM is more flexible to conduct the model follow a determined situation. In

commercial functions, it is likely that agents have decision making or negotiation

functions, which are difficult to translate straightforward into flows and levels

patterns of EBM. For instance, in our ABM, consumers and dealers communicate to

each other more than once in one process such as the consumers have to ask for

items before the dealer send item back. In EBM, we can only create two flows, one

for money and the other for products and they will run at the same time following

the equations. Moreover in the sense of network, we can not build EBM to represent

the network relation between agents. In ABM we can set the number of consumers

communicating with dealers to create a network. Even though in our scenario both

EBM and ABM can have the function of decision-making such as in experiment3,

63

in more complicated model, it seems more difficult for EBM to include such

feature.

• ABM is more realistic, especially ABM in a distributed or concurrent

implementation. This issue can be discussed in 2 points, first is comparing to EBM

and second is comparing to other ABMs. In business, it is more practical to study

behaviour of population individually, which is opposite to the approach of EBM.

The same kinds of components, which EBM see them as a collection, actually they

have their individual behaviour. From the result of ABM Experiment 2, even though

all dealers are in the same environment, their price and profit indicate different

value. The second issue that makes our ABM more realistic comparing to other

methodology of ABM is that our ABM is based on distributed simulation. In real

world, communication between organisations happens simultaneously rather than

sequentially. For instance, when consumers are looking for dealers to supply their

resources, they definitely do not contact only one dealer at one time in order to

select the dealer who offer the best solution and fulfill their requirement.

Furthermore at the same time, this scenario happens to other pairs of consumers and

dealers. In our ABM, each agent represents one organisation that is independent and

is executed in parallel. They can perform their activity individually as in our model.

The consumer sends ‘ask_product’ to all related dealers at one time, and each dealer

communicates with and supplies products to all related consumers at one time.

However when either consumer or dealer chooses to communicate with any dealers

or consumers, only one consumer and one dealer can communicate each other at one

time and simultaneous.

• ABM provides individual analysis. Both EBM and ABM give the similar result in

the system level but in ABM, we can analyse at an individual level. Each agent

behaves individually and independently and gives different responses to other

agents. Consequently, we can see individual behaviour causing a reaction with each

other.

64

• ABM is more practical to be used in companies. As the benefit of simulation,

manager can play with the simulation to simulate some aspects in real business.

With ABM, it will give a practical view and real life in business because it can

simulate the event of sending a message and waiting for response including some

negotiation and decision-making.

5.2 Future Work

The following list contains some ideas about a future version of this approach to

enhance its performance and realism.

• This model consists of three tiers of supply networks, which are consumers dealers

and a producer. We can extend the model to be n-tier supply networks. For instance

we may include manufacturers and assemblers following an hourglass model.

• We can adjust some parts of code in order to make the model more realistic such as

in the part of a producer agent, we can determine a specific price to be increased at

every tick. In a dealer part, we may use first arrived, first served as its policy, for

instance, C1 and C2 may arrive at respective simulation times t1 < t2 which causes

C1 to always receive offer faster than C2 but they may arrive in either order. We

can choose a message from tuple space by checking the order and pick it up

sequentially rather than using a respective simulation time.

• In order to make the simulation user friendly, we can develop the front-end for this

model. This can be done by using Tcl/Tk, which is a language and a library to be

embedded in application programs and provide graphic interface. Sicstus Prolog

provide the tcltk library package, which is an interface to the Tcl/Tk language and

toolkit.

65

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Appendix A

Program code % --------------------------------------------------------------------------------------------------- %File: Consumer.pl %Author: Wanwimol T. %Update:10 Sept, 2001 % --------------------------------------------------------------------------------------------------- :-[preamble]. :-[linda_part]. :-[common_part]. :-[msg_primitives]. %----------------------------------------------------------------------------------------------------- %A consumer checks whether there is at least one dealer is alive. loop(Budget):- identity(Ag), allDealers(Dealers), out(m(adm,Dealers)), check_dealer(Ag,Dealers,ND,NL), ND = 0, '!'(m(Ag,adm,log([die(Ag,Budget)]))). %----------------------------------------------------------------------------------------------------- %A consumer processes a transaction with dealers.

%The consumer checks whether there is at least one dealer alive and then sent

‘ask_order’ %message. The consumer waits for an ‘offer’ message including sell price

from each dealer. The %consumer compares the offer price , chooses the dealer with

which it will buy a product and send %‘confirm’ message. The consumer sends ‘refuse’

to the others and waits for either %‘order_processed’ or ‘lot_out’ message. If it

receives ‘order_processed’ message, the consumer %will calculate new budget.

loop(Budget):- identity(Ag), allDealers(Dealers), check_dealer(Ag,Dealers,ND,NL), out(m(adm,conwillask(Ag,ND,NL))), ask_order(Ag,Dealers,NL,AL), Will_buy_price is 0, Count is 0, collect_msg(Ag,Count,Will_buy_price,Potential_dealer,Oitem,Qty,ND,NW,NP,AL), Price is Qty * NW, check_zero(Ag,NP,Qty,Price,Payprice),

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NewBudget is Budget + Payprice, out(m(adm,newbudget(NewBudget))), loop(NewBudget). %----------------------------------------------------------------------------------------------------- %This procedure is to check whether there is live dealer, and gives a list of dead dealers check_dealer(Ag,[D|Dn],ND,NL):- check_dealer(Ag,[D|Dn],0,[],ND,NL). check_dealer(Ag,[],Dsurv,Dlist,ND,NL):- ND is Dsurv, NL = Dlist. check_dealer(Ag,[D|Dn],Dsurv,Dlist,ND,NL) :- rd_noblock(m(D,Ag,no_product)), out(m(adm,noprodms(Ag,D))), add(D,Dlist,NDlist), check_dealer(Ag,Dn,Dsurv,NDlist,ND,NL). check_dealer(Ag,[D|Dn],Dsurv,Dlist,ND,NL):- NDsurvive is Dsurv + 1, check_dealer(Ag,Dn,NDsurvive,Dlist,ND,NL). add(X,[],[X]). add(X,[H|T],[H,X|T]). mem(X,[X|_]). mem(X,[_|T]):- mem(X,T). %----------------------------------------------------------------------------------------------------- %This procedure is to send “ask_order” message to all live dealers ask_order(Ag,[D|Dn],NL,AL):- ask_order(Ag,[D|Dn],NL,[],AL). ask_order(Ag,[],NL,Alist,AL):- AL = Alist. ask_order(Ag,[D|Dn],NL,Alist,AL):- member(D,NL), ask_order(Ag,Dn,NL,Alist,AL),!. ask_order(Ag,[D|Dn],NL,Alist,AL):- out(m(Ag,D,ask(1))), add(D,Alist,Alist1), ask_order(Ag,Dn,NL,Alist1,AL). %----------------------------------------------------------------------------------------------------- %This procedure will give a list of related dealers

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allDealers(Dealers):- addressees(Addr), setof(dealer(D),member(dealer(D),Addr),Dealers). %----------------------------------------------------------------------------------------------------- %This procedure is to send ‘confirm’ and ‘refuse’ message. check_zero(Ag,NP,Qty,Price,Payprice):- Qty = 0, Payprice is 0, NP = alldead. check_zero(Ag,NP,Qty,Price,Payprice):- Qty > 0, out(m(Ag,NP,confirm(Qty,Price))), out(m(adm,confirmzero(Ag,NP,Qty,Price))), getItem(Ag,NP,Price,Payprice). check_zero(Ag,NP,Qty,Price,Payprice):- Qty = 0, Payprice is 0, out(m(Ag,NP,refuse)), out(m(adm,refusezero(Ag,NP))). %----------------------------------------------------------------------------------------------------- getItem(Ag,NP,Price,Payprice):- getItem(Ag,NP,Price,Payprice,0). getItem(Ag,NP,Price,Payprice,1). getItem(Ag,NP,Price,Payprice,0):- in_noblock(m(NP,Ag,order_processed)), Payprice is Price, getItem(Ag,NP,Price,Payprice,1). getItem(Ag,NP,Price,Payprice,0):- in_noblock(m(NP,Ag,lot_out)), Payprice is 0. getItem(Ag,NP,Price,Payprice,1). getItem(Ag,NP,Price,Payprice,0):- getItem(Ag,NP,Price,Payprice,0). %----------------------------------------------------------------------------------------------------- %This procedure is to compare the offer and to choose an appropriate dealer collect_msg(Ag,0,Will_buy_price,Potential_dealer,Oitem,Qty,0,NW,NP,[]):- Qty is 0, NW is 0, NP = alldead. %for last loop collect_msg(Ag,Count,Will_buy_price,Potential_dealer,Oitem,Qty,ND,NW,NP,[]):- Count >= ND,

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NW is Will_buy_price, NP = Potential_dealer, Qty is Oitem. collect_msg(Ag,Count,Will_buy_price,Potential_dealer,Oitem,Qty,ND,NW,NP,[D|Dn]):- Count < ND, buy_or_not(Ag,Count,Will_buy_price,Potential_dealer,Oitem,Qty,ND,NW,NP,[D|Dn]). %----------------------------------------------------------------------------------------------------- buy_or_not(Ag,Count,Will_buy_price,Potential_dealer,Oitem,Qty,ND,NW,NP,[D|Dn]):- Will_buy_price = 0, ND = 1, wait4offer(D,Ag,Item,UnitPrice), NCount is Count + 1, collect_msg(Ag,NCount,UnitPrice,D,Item,Qty,ND,NW,NP,Dn). buy_or_not(Ag,Count,Will_buy_price,Potential_dealer,Oitem,Qty,ND,NW,NP,[D|Dn]):- Will_buy_price = 0, wait4offer(D,Ag,Item,UnitPrice), NCount is Count + 1, collect_msg(Ag,NCount,UnitPrice,D,Item,Qty,ND,NW,NP,Dn). buy_or_not(Ag,Count,Will_buy_price,Potential_dealer,Oitem,Qty,ND,NW,NP,[D|Dn]):- Will_buy_price > 0, wait4offer(D,Ag,Item,UnitPrice), compare_price(Ag,D,UnitPrice,Item,Will_buy_price,Potential_dealer,Oitem,CW,CP,CI), NCount is Count + 1, collect_msg(Ag,NCount,CW,CP,CI,Qty,ND,NW,NP,Dn). %----------------------------------------------------------------------------------------------------- wait4offer(D,Ag,Item,UnitPrice):- wait4offer(D,Ag,Item,UnitPrice,0). wait4offer(D,Ag,Item,UnitPrice,1):- Item is Item, UnitPrice is UnitPrice. wait4offer(D,Ag,Item,UnitPrice,I):- I = 0, rd_noblock(m(D,Ag,offer(Item,UnitPrice))), in(m(D,Ag,offer(Item,UnitPrice))), wait4offer(D,Ag,Item,UnitPrice,1). wait4offer(D,Ag,Item,UnitPrice,I):- I = 0, rd_noblock(m(D,Ag,no_product)), out(m(D,Ag,offer(0,10))), in(m(D,Ag,offer(Item,UnitPrice))), wait4offer(D,Ag,Item,UnitPrice,1).

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wait4offer(D,Ag,Item,UnitPrice,I):- I = 0, wait4offer(D,Ag,Item,UnitPrice,0). %----------------------------------------------------------------------------------------------------- compare_price(Ag,D,UnitPrice,Item,Will_buy_price,Potential_dealer,Oitem,CW,CP,CI):- compare_price(Ag,D,UnitPrice,Item,Will_buy_price,Potential_dealer,Oitem,CW,CP,CI,0). compare_price(Ag,D,UnitPrice,Item,Will_buy_price,Potential_dealer,Oitem,CW,CP,CI,F):- F = 1, CW is Will_buy_price, CP = Potential_dealer, CI is Oitem. compare_price(Ag,D,UnitPrice,Item,Will_buy_price,Potential_dealer,Oitem,CW,CP,CI,F):- Will_buy_price =< UnitPrice, out(m(Ag,D,refuse)), out(m(adm,refusecp1(Ag,D))), compare_price(Ag,D,UnitPrice,Item,Will_buy_price,Potential_dealer,Oitem,CW,CP,CI,1). compare_price(Ag,D,UnitPrice,Item,Will_buy_price,Potential_dealer,Oitem,CW,CP,CI,F):- Will_buy_price > UnitPrice, out(m(Ag,Potential_dealer,refuse)), out(m(adm,refusecp2(Ag,Potential_dealer))), compare_price(Ag,D,UnitPrice,Item,UnitPrice,D,Item,CW,CP,CI,1). %----------------------------------------------------------------------------------------------------- %The command is to execute the producer agent. The initial value is loop(InitBudget) :-initialise,rd(m(adm,all,start)), loop(0),terminate. % -------------------------------------------------------------------------- % EOF -- EOF -- EOF -- EOF -- EOF -- EOF -- EOF -- EOF % -------------------------------------------------------------------------- % --------------------------------------------------------------------------------------------------- %File: Dealer.pl %Author: Wanwimol T. %Update:10 Sept, 2001 % --------------------------------------------------------------------------------------------------- :-[preamble]. :-[linda_part]. :-[msg_primitives]. :-[common_part].

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%------------------------------------------------------ %Case1: A dealer’s stock < a safety stock limit and a producer died %When a dealer’s stock is less than a stock limit, the dealer has to check whether a producer is %alive. If it finds ‘out_of_stock’ message from the producer, which means the producer died, the %dealer will send message ‘no_product’ to it customers and will die. We get a list of the dealer’s %profit from here. loop(Stock,Margin,[Profit|PL],Stockleft,Price):- Stockleft < Stock, identity(Ag), rd_noblock(m(P,Ag,out_of_stock)), %% out(m(adm,gotmsproddie(Ag))), sendAllMs(no_product), rev1([Profit|PL],W), out(m(adm,log(W))), out(m(adm,log([die(Ag)]))), out(m(adm,die(Ag))). %------------------------------------------------------ %Case2: A dealer’s stock < a safety stock limit and a producer is alive %When a dealer’s stock is less than a stock limit, the dealer has to check whether a producer is %alive. If the producer is alive, the dealer will send ‘ask_stock’ to order products to fulfill its %stock. New stock, cost, new profit, new price are calculated here. loop(Stock,Margin,[Profit|PL],Stockleft,Price):- Stockleft < Stock, identity(Ag), addressees(Addrs), member(producer(P),Addrs), out(m(Ag,producer(P),ask_stock(Stock))), receiveStock(producer(P),Ag,Profit,Stockleft,Price,NewProfit,NewStockleft,NewPrice,Margin), add(NewProfit,[Profit|PL],ProfitL), loop(Stock,Margin,ProfitL,NewStockleft,NewPrice). %------------------------------------------------------ %Case3: A dealer has enough stock to process it transactions with its customers %The dealer wait for requests from customers and send back its offers including price. The dealer %waits for ‘confirm’ or ‘refuse’ message from customer. If the offer is confirmed, the dealer will %check its stock. If the dealer has enough stock for that order, it will send ‘offer_ processed’ and %wait for ‘stock_receive’ message. If the dealer has not enough stock, which comes from that %order message is checked later than others, the dealer will send ‘lot_out’ message. New stock, %revenue and new profit are calculated here. loop(Stock,Margin,[Profit|PL],Stockleft,Price):- receiveRequests(Price), confirm_or_not(Price,Stockleft,Profit,NewStockleft,NewProfit), add(NewProfit,[Profit|PL],ProfitL), loop(Stock,Margin,ProfitL,NewStockleft,Price). loop(Stock,Margin,[Profit|PL],Stockleft,Price):- loop(Stock,Margin,ProfitL,NewStockleft,Price). %---------------------------------------------------------------- % The procedure is receiving a stock from the producer and calculating cost, new stock and

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% new profit. receiveStock(producer(P),Ag,Profit,Stockleft,Price,NewProfit,NewStockleft,NewPrice,Margin):- receiveStock(producer(P),Ag,Profit,Stockleft,Price,NewProfit,NewStockleft,NewPrice,Margin,0). receiveStock(producer(P),Ag,Profit,Stockleft,Price,NewProfit,NewStockleft,NewPrice,Margin,1). receiveStock(producer(P),Ag,Profit,Stockleft,Price,NewProfit,NewStockleft,NewPrice,Margin,0):- in_noblock(m(producer(P),Ag,offer_stock(Add2stock,Cost))), out(m(Ag,producer(P),stock_received)), Ovcost(OV) UCost is Cost/Add2stock, NewPrice is (Margin*UCost)+UCost, NewProfit is Profit - Cost - OV, NewStockleft is Stockleft + Add2stock, receiveStock(producer(P),Ag,Profit,Stockleft,Price,NewProfit,NewStockleft,NewPrice,Margin,1). receiveStock(producer(P),Ag,Profit,Stockleft,Price,NewProfit,NewStockleft,NewPrice,Margin,0):- rd_noblock(m(producer(P),Ag,out_of_stock)), NewPrice is Price, NewProfit is Profit, NewStockleft is Stockleft, receiveStock(producer(P),Ag,Profit,Stockleft,Price,NewProfit,NewStockleft,NewPrice,Margin,1). receiveStock(producer(P),Ag,Profit,Stockleft,Price,NewProfit,NewStockleft,NewPrice,Margin,0):- receiveStock(producer(P),Ag,Profit,Stockleft,Price,NewProfit,NewStockleft,NewPrice,Margin,0). %------------------------ add(X,[],[X]). add(X,[H|T],[X,H|T]). rev1([],[]). rev1([H|T],L):-rev1(T,Z),append1(Z,[H],L). append1([],S,S). append1([X|S1],S2,[X|S3]):-append1(S1,S2,S3). %---------------------------------------------------------------

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% receiveRequests(Requests/-) % this procedure obtains a list of requests for items from consumer agents receiveRequests(Rs):- identity(Ag), addressees(Addrs), my_setof(consumer(C), member(consumer(C),Addrs),Cs), receiveRequestsAux(Cs,Ag,Rs). receiveRequestsAux([],_,Rs). receiveRequestsAux([C|Cs],Ag,Rs):- in(m(C,Ag,ask(1))), out(m(Ag,C,offer(1,Rs))), receiveRequestsAux(Cs,Ag,Rs). receiveRequestsAux([_|Cs],Ag,Rs):- receiveRequestsAux(Cs,Ag,Rs). % --------------------------------------------------------------------------------------------------- %This procedure sends message M to related consumers. sendAllMs(M):- identity(Ag), allCons(Cons), sendAllMsAux(Cons,Ag,M). sendAllMsAux([],_,_). sendAllMsAux([C|Cs],Ag,M):- out(m(Ag,C,M)), out(m(adm,dealerwilldie(Ag,C,M))), sendAllMsAux(Cs,Ag,M). allCons(Cons):- addressees(Addr), setof(consumer(C),member(consumer(C),Addr),Cons). %------------------------------------------------------------------ % This procedure sends this message to the producer(P) requestOrders([]). requestOrders(Orders):- identity(Ag), addressees(Addrs), member(producer(P),Addrs), '!'(Ag,producer(P),request(Orders)). %----------------------------------------------------- %This procedure checks whether consumers send ‘confirm’ or ‘refuse’ message confirm_or_not(Price,Stockleft,Profit,NewStockleft,NewProfit):- identity(Ag), addressees(Addrs), my_setof(consumer(C),

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member(consumer(C),Addrs),Cs), confirmAux(Cs,Ag,Price,Stockleft,Profit,NewStockleft,NewProfit). %---------------------------------------------------------------- % When the dealer receive ‘confirm’ message, it will check its stock.. confirmAux([],Ag,Price,Stockleft,Profit,NewStockleft,NewProfit):- NewStockleft is Stockleft, NewProfit is Profit. confirmAux([C|Cs],Ag,Price,Stockleft,Profit,NewStockleft,NewProfit):- in_noblock(m(C,Ag,confirm(Qty,Price))), check_stock(C,Ag,Qty,Price,Stockleft,Profit,NSL,NPF), confirmAux(Cs,Ag,Price,NSL,NPF,NewStockleft,NewProfit). confirmAux([C|Cs],Ag,Price,Stockleft,Profit,NewStockleft,NewProfit):- in_noblock(m(C,Ag,refuse)), NSL1 is Stockleft, NPF1 is Profit, confirmAux(Cs,Ag,Price,NSL1,NPF1,NewStockleft,NewProfit). confirmAux([C|Cs],Ag,Price,Stockleft,Profit,NewStockleft,NewProfit):- confirmAux([C|Cs],Ag,Price,Stockleft,Profit,NewStockleft,NewProfit). %----------------------------------------------------------------- %If the dealer has enough stock, it will send order_processed message, deduct its stock %and calculate new profit. If the dealer has not enough stock, it will send ‘lot_out’ message check_stock(C,Ag,Qty,Price,Stockleft,Profit,NSL,NPF):- Stockleft >= Qty, %%%out(m(adm,checkstock>order(Ag))), out(m(Ag,C,order_processed)), out(m(adm,order_processed(Ag,C))), NSL is Stockleft - 1, NPF is Profit + Price. check_stock(C,Ag,Qty,Price,Stockleft,Profit,NSL,NPF):- Stockleft < Qty, out(m(Ag,C,lot_out)), out(m(adm,lot_out(Ag,C))), NSL is Stockleft, NPF is Profit. % -------------------------------------------------------------------------- % EOF -- EOF -- EOF -- EOF -- EOF -- EOF -- EOF -- EOF % -------------------------------------------------------------------------- % --------------------------------------------------------------------------------------------------- %File: Dealer1_breed.pl %Author: Wanwimol T. %Update:10 Sept, 2001 % --------------------------------------------------------------------------------------------------- % This is to initial a variable : FixedCose

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% ovcost(0.05). %The command is to execute the dealer agent. %The initial value is loop(SafetyLevel,MarginOfProfit,InitProfit,InitStock,InitPrice) :- initialise,rd(m(adm,all,start)),loop(5,1.5,[0],0,0) ,terminate % -------------------------------------------------------------------------- % EOF -- EOF -- EOF -- EOF -- EOF -- EOF -- EOF -- EOF % -------------------------------------------------------------------------- % --------------------------------------------------------------------------------------------------- %File: Producer.pl %Author: Wanwimol T. %Update:10 Sept, 2001 % --------------------------------------------------------------------------------------------------- :-[preamble]. :-[linda_part]. %------------------------------------------------------ %Case1: A producer has not enough stock. %When a producer’s stock is less than 5, the producer will send die-message. This number should %be equal to the maximum delivery lot, if there is more than one breed of dealers. loop(Stock):- Stock < 5, identity(Ag), out(m(adm,producerwilldie(Ag))), sendAllRequest(offer_stock(0,0)), sendAllRequest(out_of_stock), out(m(adm,producerDIEDDDDD(Ag))), out(m(adm,log([die(Ag)]))), out(m(adm,die(Ag))). %------------------------------------------------------ %Case2: A producer has enough stock. %A producer waits for ‘ask_stock’ message from dealers. If there is any ‘ask_stock’ message, the %producer will send ‘offer_stock’ message including quantity and price. After that the producer %waits for ‘stock_receive’ message from the dealers, and calculates a new stock loop(Stock):- Stock >= 5, identity(Ag), in_noblock(m(D,Ag,ask_stock(Add2stock))), ProdPrice = 1/Stock, SalePrice = Add2stock * ProdPrice, out(m(Ag,D,offer_stock(Add2stock,SalePrice))), out(m(adm,offer_stock(Ag,D,Add2stock,SalePrice) ) ), in(m(D,Ag,stock_received)),

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out(m(adm,gotreceivems(D))), NewStock = Stock - Add2stock, write(NewStock), out(m(adm,stockleft(NewStock))), loop(NewStock). loop(Stock):- loop(Stock). %---------------------------------- sendAllRequest(M):- identity(Ag), allDealers(Dealers), sendAllRequestAux(Dealers,Ag,M). sendAllRequestAux([],_,_). sendAllRequestAux([D|Ds],Ag,M):- out(m(Ag,D,M)), out(m(adm,willdie(Ag,D,M))), sendAllRequestAux(Ds,Ag,M). %--------------------------------------------------------------------------- allDealers(Dealers):- addressees(Addr), setof(dealer(D),member(dealer(D),Addr),Dealers). %---------------------------------------------------------------------------- receiveOffers(Offers):- identity(Ag), allDealers(Dealers), receiveOffersAux(Dealers,Ag,Offers). receiveOffersAux([],_,[]). receiveOffersAux([D|Ds],Ag,[m(D,Ag,offer(Qty,Price))|Offers]):- in_noblock(m(D,Ag,offer(Qty,Price))), receiveOffersAux(Ds,Ag,Offers). receiveOffersAux([D|Ds],Ag,Offers):- rd_noblock(m(D,dead)), receiveOffersAux(Ds,Ag,Offers). receiveOffersAux(Ds,Ag,Offers):- receiveOffersAux(Ds,Ag,Offers). %--------------------------------------------------------------------------- %The command is to execute the producer agent. The initial value is loop(ProducerStock) :- initialise,rd(m(adm,all,start)),loop(500),terminate. % -------------------------------------------------------------------------- % EOF -- EOF -- EOF -- EOF -- EOF -- EOF -- EOF -- EOF % -------------------------------------------------------------------------- % --------------------------------------------------------------------------- % Program: experiment.pl

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% Date: 04/06/2001 % Author: Wamberto W. Vasconcelos Adaped by: Wanwimol Thawornchak % Purpose: % This program builds and runs a multi-agent system from a specification. % --------------------------------------------------------------------------- :- use_module(library(lists)), use_module(library(system)), use_module(library(random)), use_module(library('linda/client')). % --------------------------------------------------------------------------- % startLPad % starts up a Linda server and connects this program as one client process startLPad:- startServer(_), initialiseClient(_). % --------------------------------------------------------------------------- % experiment(Exp/?,FileName/+) % runs experiment Exp and stores information on the run in FileName, a log % file. FileName *must* be provided. If a value for Exp is provided then % only the experiment with that name is run, otherwise we run the first % experiment. experiment(Exp,FileName):- setof(Label-MAS,mas(Exp,Label,MAS),MASs), % obtains all experiments open(FileName,append,Stream), % FileName is open nl(Stream), write(Stream,experiment:Exp), nl(Stream), simulateMASs(MASs,Stream), % run them nl(Stream), close(Stream). % close FileName % --------------------------------------------------------------------------- simulateMASs([],_):- % at end cleanTuple. % clean tuple space of messages simulateMASs([Label-MAS|MASs],Stream):- % for each Label-MAS nl, write('-----------------------'), write(Label), write('-----------------------'), startUp(MAS), % start up MAS nl(Stream), write(Stream,'% '), write(Stream,Label), supervise(Stream), % supervise execution cleanTuple, % clean tuple space simulateMASs(MASs,Stream). % do the same with rest... % ---------------------------------------------------------------------------

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% mas(ExpName/+,Label/+,MAS/-) % ExpName is the name of the experiment. It has Label which will be used in % to identify the experiment in the log file. MAS is a list of runnable % Prolog programs -- these are the agents that will populate experiment % ExpName. ExpName and Label are just names, so there is nothing important % about their choice % NB.: The definitions of mas/3 given below are illustrative. They should be % adapted to different needs and experiments. The important thing to notice % is how populations can be related automatically. mas(exp1,label(exp1,c:20,all-all,d:4,all-all,p:1),MAS):- population(consumer, % define population of consumers 'consumer.pl', % using this species 'consumer_breed.pl', % and this breed 20,Cons), % 20 agents stored in list Cons population(dealer1, % define population of dealers 'dealer.pl', % using this species 'dealer1_breed.pl', % and this breed 2,Dealers1), % 2 agents stored in list Dealers population(dealer2, % define population of dealers 'dealer.pl', % using this species 'dealer2_breed.pl', % and this breed 2,Dealers2), % 2 agents stored in list Dealers population(producer, % define population of a producer 'producer.pl', % using this species 'producer_breed.pl', % and this breed 1,Producers), relate(Producers:all, Dealers1:all, ProducersR,Dealers1R), relate(ProducersR:all, Dealers2:all, ProducersRR,Dealers2R), relate(Dealers1R:all, Cons:all, Dealers1RR,ConsR), relate(Dealers2R:all, ConsR:all, Dealers2RR,ConsRR), append(ProducersRR,Dealers1RR,TmpPop1), % assemble list of all agents append(Dealers2RR,ConsRR,TmpPop2), append(TmpPop1,TmpPop2,MAS). % --------------------------------------------------------------------------- % population(Name/+,Species/+,Breed/+,Qty/+,Inds/-) % creates a population of Qty (an integer) agents of kind Name. Each agent

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% is a list of the form % [identity(Id),addressees([]),(:-Species),(:-Breed)] % where Id is Name(No). The agents are stored in list Inds. population(Name,Species,Breed,Qty,Inds):- populationAux(1,Qty,Name,Species,Breed,Inds). % initial call populationAux(Ctr,Qty,_,_,_,[]):- % Qty has been reached Ctr > Qty, !. populationAux(Ctr,Qty,Name,Species,Breed,[I|Is]):- Id =.. [Name,Ctr], I = [identity(Id),addressees([]), (:-[Species]),(:-[Breed])], NewCtr is Ctr + 1, populationAux(NewCtr,Qty,Name,Species,Breed,Is). % --------------------------------------------------------------------------- % relate(IndL:OpL/+,IndR:OpR/+,IndLR/-,IndRR/+) % relates OpL agents of list IndL with OpR agents of list IndR. OpL and OpR % can be "all", "some" or any integer. The relationship is forged by % including the identity(Id) of the OpR agents in IndR into the fact % addressees(Addr) of OpL agents in IndL. The relationship should be % commutative -- if an agent ag1 has agent ag2 as an addressee, then % agent ag2 should also have ag1 as an addressee. relate(IndL:OpL,IndR:OpR,IndLR,IndRR):- getIds(IndL,IdsL), % get identifiers of IndL getIds(IndR,IdsR), % get identifiers of IndR relateIds(IdsL:OpL,IdsR:OpR,Graph), % build graph relating identifiers relateAux(IndL,Graph,IndLR), % distribute info of Graph relateAux(IndR,Graph,IndRR), % distribute info of Graph !. relateAux([],_,[]). % end of Inds? relateAux([Ind|Inds],Graph,[RInd|RInds]):- % for each Ind member(identity(Id),Ind), % get hold of identity(Id) my_setof(L,member(L-Id,Graph),LNs), % get all edges in Graph w/ L-Id my_setof(R,member(Id-R,Graph),RNs), % get all edges in Graph w/ Id-R append(LNs,RNs,Ns), % append the Ls and Rs remove_duplicates(Ns,NNs), % remove any duplicates addNodes(Ind,NNs,RInd), % add new addressees to Ind relateAux(Inds,Graph,RInds). % do the same for other Inds % --------------------------------------------------------------------------- % getIds(ListAgs/+,ListIds/-) % obtains ListIds, a list of identity(Id), in ListAgs. getIds([],[]). getIds([I|Is],[Id|Ids]):- member(identity(Id),I), getIds(Is,Ids).

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% --------------------------------------------------------------------------- % addNodes(Ind/+,Ids/+,NewInd/-) % given an agent Ind, this routine adds Ids to the fact addressees(Addr). % In order to preserve the order of facts/rules in Ind, we have to traverse % the list. addNodes([],_,[]). addNodes([addressees(Addr)|Rs],NNs,[addressees(NewAddr)|Rs]):- !, append(Addr,NNs,NewAddr). addNodes([R|Rs],NNs,[R|NRs]):- addNodes(Rs,NNs,NRs). % --------------------------------------------------------------------------- % relateIds(IdL:OpL/+,IdR:OpR/+,Graph/-) % this procedure does the actual relating between the identifiers of agents. % The relationships are recorded in Graph as a list of edges L-R where L and % R are identifiers from IdL and IdR, respectively. % IdL and IdR are lists of identifiers (names of agents). This procedure % relates OpL (all, some or N) elements of IdL to OpR elements of IdR. An % element L is related to an element R if an entry L-R appears in Graph. relateIds(IdL:OpL,IdR:OpR,Graph):- selectIds(OpL,IdL,SIdL), % select OpL ids from IdL and relateIdsAux(SIdL,OpR,IdR,[],Graph). % relates them with OpR ids of IdR relateIdsAux([],_,_,G,G). % no more IdL... relateIdsAux([IdL|IdLs],OpR,IdR,TempG,G):- % for each selected IdL selectIds(OpR,IdR,SIdR), % select OpR IdR edges(SIdR,IdL,Edges), % build edges with them append(Edges,TempG,NTempG), % append new edges to TempG relateIdsAux(IdLs,OpR,IdR,NTempG,G). % do the same for IdLs % --------------------------------------------------------------------------- % selectIds(Op/+,Ids/+,SIds/-) % selects Op (all, some or integer N) elements from list Ids and stores them % in list SIds selectIds(all,L,L):- % select all... !. selectIds(some,L,SomeL):- % to select some elements from L... length(L,Size), % count number of elements randomSize(Size,RSize), % get a random number up to no. of elements selectIds(RSize,L,SomeL), % select this random number from L !. selectIds(some,L,SomeL):- % clause above may fail (because of random) selectIds(some,L,SomeL). % so try again (eventually it works...) selectIds(0,_,[]):- % to select 0 elements... !.

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selectIds(N,[X|Xs],[X|NXs]):- % to select N elements random(T), % randomly choose a number T > 0.5, % if chose number > 0.5 NN is N - 1, % decrease counter selectIds(NN,Xs,NXs). % select more... selectIds(N,[_|Xs],NXs):- % if random number =< 0.5 selectIds(N,Xs,NXs). % skip element and try to get more... % --------------------------------------------------------------------------- % randomSize(Size/+,RSize/-) % obtains a positive random integer RSize less than or equal to Size randomSize(Size,RSize):- random(N), % get random number N (it's a decimal...) RSize is integer(N * 10), % multiply N by 10 and get the integer bit RSize > 0, % it must be greater than 0 RSize =< Size, % and less than or equal to Size !. randomSize(Size,RSize):- % if not possible, try again randomSize(Size,RSize). % (clause above may fail...) % --------------------------------------------------------------------------- % edges(ListIdsL/+,IdR/+,ListEdges/-) % builds a graph in ListEdges as a list of pairs IdL-IdR where IdL is an % element of ListIdsL. edges([],_,[]). edges([N|Ns],M,[N-M|Es]):- edges(Ns,M,Es). % --------------------------------------------------------------------------- % my_setof(A/-,B/+,C/-) % personal definition of setof that returns empty list if B has no answers. my_setof(A,B,C):- setof(A,B,C), % my_setof is setof... !. my_setof(_,_,[]). % but if it fails then we get the empty list % --------------------------------------------------------------------------- % startUp(Ags/+) % starts up the list of runnable Prolog programs Ags as background processes. startUp(Ags):- createFiles(Ags,0,Files), % create files with contents startUpAux(Files), % start them up sleep(5), deleteFiles(Files). % delete files startUpAux([]). % end of agents startUpAux([F|Fs]):- % for each file name F name('echo "[',P1), % * encode UNIX command with name(F,P2), % * its name

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name(']." | sicstus > /dev/null &',P3), % * this is the rest of command append(P1,P2,P12), % the * above are ASCII characters append(P12,P3,ASCCommand), % [operating on a character level] name(Command,ASCCommand), % get actual command exec(Command,[null,null,null],_), % execute it startUpAux(Fs). % do the same with other files % --------------------------------------------------------------------------- % createFiles(Ags/+,N/+,FileNames/-) % given the list Ags of Prolog programs (list of lists) and the counter N, % write the content of each element in Ags onto a file and store its name % in list FileNames createFiles([],_,[]). % end? createFiles([Ag|Ags],N,[File|Files]):- % for each agent Ag (Prolog progr) name(N,ASCN), % get the ASC for N name(File,[97,103|ASCN]), % assemble the name of the file open(File,write,Line), % open the file writeFile(Ag,Line), % write the contents of Ag in it close(Line), % close file NN is N + 1, % increment counter createFiles(Ags,NN,Files). % do the same with Ags % --------------------------------------------------------------------------- % writeFile(ListClauses/+,Line/+) % writes ListClauses in Line writeFile([],_). writeFile([L|Ls],Line):- portray_clause(Line,L), writeFile(Ls,Line). % --------------------------------------------------------------------------- % deleteFiles(FileNames/+) % deletes the files whose names are specified in the list FileNames deleteFiles([]). % while it's not empty list... deleteFiles([F|Fs]):- % for each file name F name('rm -f ',P1), % assemble a command (ASCII...) name(F,P2), append(P1,P2,ASCCommand), name(Command,ASCCommand), % translate ASCII onto command exec(Command,[null,null,null],_), % execute it deleteFiles(Fs). % do the same with other Fs.. % --------------------------------------------------------------------------- % startServer(PId):- % this routine is responsible for generating and running the Linda server % process. The server program is stored in the file "server.pl" and is % created automatically. % When the server process is started, it creates a file "server.addr" which

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% works as a Linda "handler" -- all client processes should have access to the % information concerning the Host:Port to which the server is "listening" and % this information is recorded in the file "server.addr". % This routine checks that the file "server.addr" is available. If the file % is found, the routine then checks that the server process is actually % running. % If the file isn't there or if the process isn't running, the routine starts % up a fresh server process. startServer(PId):- file_exists('server.addr'), % if file "server.addr" (handle) exists see('server.addr'), % open it, read(_:_-PId), % get PId, seen, % close it, and up(PId), % check that process is up !. startServer(PId):- % otherwise... createServerProgram, % create server program "server.pl" startServerAux, % start it up sleep(5), % wait a bit... (to update file) see('server.addr'), % open file with Linda handle read(_:_-PId), % get PId seen. % close it startServerAux:- exec('echo "[\'server.pl\']." | sicstus > /dev/null &', [null,null,null], _). % -------------------------------------------------------------------------- % up(+PId):- % holds if process PId is running up(PId):- concat(['ps -p ',PId,' | wc'],Command), % type this command in UNIX exec(Command,[null,pipe(Out),null],_), % and PId will be running get(Out,50). % if first returned Char is 2 createServerProgram:- tell('server.pl'), write(':- use_module(library(\'linda/server\')),'),nl, write(' use_module(library(\'system\')),'),nl, write(' pid(PId),'),nl, write(' linda((Host:Port)-(tell(\'server.addr\'),'),nl, write(' '), write('write(\''),write(\),write('\'\'),'),nl, write(' '), write('write(Host),'),nl, write(' '), write('write(\''),write(\),write('\':\'),'),nl, write(' '),

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write('write(Port-PId),'),nl, write(' '), write('write(\'.\'),'),nl, write(' '), write('told)).'), told. % -------------------------------------------------------------------------- % initialiseClient(PId):- % starts up a connection between this program (which is a process) and the % other processes it starts up automatically. This predicate gets the process % id of the server process. % % NB: this routine is automatically executed whenever this program is loaded! initialiseClient(PId):- see('server.addr'), read(Host:Port-PId), seen, linda_client(Host:Port). % -------------------------------------------------------------------------- % killProcess(+ProcDescription):- % kills process(es) fitting ProcDescription. ProcDescription can be a list of % descriptors, or any of the descriptors process(AgName,PId), all, server, PId, % AgName. Appropriate messages are issued if things go wrong... killProcess([]). killProcess([Proc|Procs]):- % if list of descriptors killProcess(Proc), % kill each of them killProcess(Procs). killProcess(all):- % if killing "all" bagof_rd_noblock(process(AgName,PId), % get all process/2 tuples process(AgName,PId), Procs), !, killProcess(Procs), % kill them all removeMsgs(Procs), % remove process/2 tuples killProcess(server). % kill server killProcess(all):- % if there are no process/2 tuples killProcess(server). % just kill server killProcess(server):- % if kill server see('server.addr'), % see handle file read(_:_-PId), % get PId seen, kill(PId,9), % kill it nl, write('Server has been killed!!'). % issue message killProcess(process(AgName,PId)):- % if process/2 descriptor

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up(PId), % and PId is up then kill(PId,9), % kill it concat(['Process ',PId, % assemble message ' with agent ', AgName, ' has been killed!!'],Msg), nl, write(Msg). % display message killProcess(PId):- % if PId descriptor integer(PId), rd_noblock(process(AgName,PId)), % check that process/2 tuple exists !, killProcess(process(AgName,PId)). % and kill it killProcess(AgName):- % if AgName descriptor rd_noblock(process(AgName,PId)), % check process/2 !, killProcess(process(AgName,PId)). % and kill it killProcess(PId):- % exceptions... concat(['Process ',PId, ' doesn\'t exist!!'],Msg), nl, write(Msg). % -------------------------------------------------------------------------- % removeMsgs(+ListOfTuples):- % deletes (using in/1) all the tuples in ListOfTuples from the tuple space removeMsgs([]). removeMsgs([M|Ms]):- in(M), removeMsgs(Ms). % -------------------------------------------------------------------------- % cleanUp:- % kills all processes and deletes all server files cleanUp:- killProcess(all), delete_file('server.pl',[ignore]), delete_file('server.addr',[ignore]), close_client. % -------------------------------------------------------------------------- % supervise:- % routine to supervise processes -- it records all messages "in transit" % in a log file % NB (1) a "adm(start)" tuple is sent to the tuple space, to be read by the % processes as the "go ahead". This design decision is also incorated % to the initialisation routine of each agent. % NB (2) the log file has a changing name in case successive experiments are % to be run (if the same name was used previous results would be lost)

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% NB (3) we make use of the following convention: tuples adm(M) are to be % sent from the processes, where "M" is the actual message and adm/1 % is an outer "layer". This layer is removed by this routine and M is % stored in the tuple space. %------------------------------------------------------------------------- supervise(Stream):- sleep(5), out(m(adm,all,start)), % start message (see above) superviseAux(Stream). % loops... superviseAux(Stream):- % loops for messages messagesInTransit(Stream), % checks for them superviseAux(Stream). % carries on loop superviseAux(_):- % loops for messages \+ rd_noblock(process(_)). % checks if all dead superviseAux(Stream):- % loops for messages %-------------------------------------------------------------------------- messagesInTransit(Stream):- % retrieves messages in_noblock(m(adm,Msg)), % that match adm(M) logMsg(Stream,Msg), nl, write(Msg), % show it on screen returnMsg(Msg). % write M in tuple space messagesInTransit(Stream):- % retrieves messages in_noblock(m(_,adm,Msg)), % that match adm(M) logMsg(Stream,Msg), nl, write(Msg), % show it on screen returnMsg(Msg). % write M in tuple space % -------------------------------------------------------------------------- logMsg(Stream,log(List)):- % logs message log(List) nl(Stream), logMsgAux(Stream,List). % logs List logMsg(_,_). % otherwise disconsider logMsgAux(_,[]). logMsgAux(Stream,[X|Xs]):- logInfo(Stream,X), % log each element logMsgAux(Stream,Xs). logInfo(Stream,Term):- % only log particular info write(Stream,Term), write(Stream,' '). logInfo(_,_). % --------------------------------------------------------------------------

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returnMsg(m(Or,Dst,M)):- % certain intercepted messages out(m(Or,Dst,M)). % have to be returned returnMsg(_). % -------------------------------------------------------------------------- % Auxiliary Routines % -------------------------------------------------------------------------- % concat(+ListStrings,?Concat):- % succeeds if Concat is the concatenation of the strings in ListStrings concat(ListStrings,Concat):- ListStrings = [Str1|Strings], concatList(Strings,Str1,Concat). concatList([],String,String). concatList([S|Ss],StringSoFar,String):- concat(StringSoFar,S,NewStringSoFar), concatList(Ss,NewStringSoFar,String). % -------------------------------------------------------------------------- % concat(+Str1+,+Str2,?Str1AndSrt2):- % succeeds if Str1AndStr2 is instantiated to the concatenation of % Str1 with Str2 concat(Str1,Str2,Str1andStr2):- name(Str1,ASCStr1), name(Str2,ASCStr2), append(ASCStr1,ASCStr2,ASCStr1andStr2), name(Str1andStr2,ASCStr1andStr2). % -------------------------------------------------------------------------- % cleanTuple % cleans tuple space by getting rid of all terms there... cleanTuple:- in_noblock(_), cleanTuple. cleanTuple. % !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! % commands executed at onset of this program... % !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! :- startServer(_), initialiseClient(_). % -------------------------------------------------------------------------- % EOF -- EOF -- EOF -- EOF -- EOF -- EOF -- EOF -- EOF % ---------------------------------------------------------