Simulation in der Logistik - uni-due.de · PDF filetu. l. Slide 1. Simulation with Arena. Prof. Dr.-Ing. Bernd Noche. Department of Engineering Sciences. Division of Mechanical Engineering

Slide 1 Simulation with Arenatul

Prof. Dr.-Ing. Bernd Noche

Department of Engineering SciencesDivision of Mechanical EngineeringTransport Systems and LogisticsLotharstraße 1 - 21 47057 Duisburg

Phone: 0203 379-2785Fax: 0203 379-3048eMail: [email protected]

UNIVERSITÄT

D U I S B U R G E S S E N

Campus Duisburg

Simulation in der Logistikehem: Simulation in Logistics II

Winter Semester Review

Lecturer: Prof. Dr.-Ing. Bernd NocheM.Sc. Nan Liu

12.04.2011 Rechnergestützte Netzanalysen 2

Source: ''Det stora världskriget'' vol II, p. 520, printed in Stockholm by Åhlén & Åkerlunds förlag, 1915

British wooden mechanical horse simulator

The General Motors Anthropomorphic Test Device


Photographs: John B. Carnett; digital imaging: Eric Heintz

Terminal 3 Flughafen Peking Gepäckförderanlagen profitieren von der Virtual Reality. Simulation ist der Schlüssel zum Erfolg – auch für die Anlage im riesigen neuen Pekinger Flughafen.

50 km Förderbandlänge: Damit beim Bau und der Inbetriebnahme der Pekinger Gepäckförderanlage (links) keine Probleme auftreten, erstellte und testete die Experte die komplexe Anlage virtuell (unten)

12.04.2011 4Rechnergestützte Modellierung


What is simulation?• Simulation is the imitation of the operation of a real‐world process or system over

time. It involves the generation of an artificial history of a system, and the observation of that artificial history to draw inferences concerning the operating characteristics of the real system.

• The behavior of a system as it evolves over time is studied by developing a simulation model. This model usually takes the form of a set of assumptions (mathematical, logical, relationships, etc.) concerning the operation of the system.

• Once developed and validated, a model can be used to investigate a wide variety of “what‐if” questions about the real world system. Thus, simulation modeling can be used both as a design tool to predict the performance of new systems under varying sets of circumstances.


When Simulation is the Appropriate Tool

• Simulation enables the study of, and experiment with, the internal interaction of a complex (sub)system.

• Changes can be simulated and the effect of these alternations on the model’s behavior can be observed.

• The knowledge gained in the designing a simulation model may be of great value toward suggesting improvement in the system under investigation.

• By changing simulation inputs and observing resulting outputs, valuable insight may be obtained: which variables are most important and how variables interacts.

• Simulation can be used to reinforce and verify analytic solution methods.• Simulation can be used experiment with new designs or policies prior to

implementation, so as to prepare for what may happen.• Animation shows a system in simulated operation so that the plan can be

visualized.• The modern system is so complex that the interaction can be treated only by

simulation


When Simulation is not Appropriate

• When the problem can be solved using common sense or analytical methods• If it is easier to perform direct experiments• If the costs exceed the savings• Simulation should not be performed if the resources or time are not available• Simulation takes data, sometimes a lot of data. If no data is available, not even

estimates, simulation is not advised• If there is not enough time or personnel are available to verify and validate the

model, simulation is not advised.• If managers have unreasonable expectations ‐ say, too much to soon – or the

power of simulation is overestimated, simulation may not be appropriate• If system behavior is too complex or can’t be defined, simulation is not appropriate


Advantages of Simulation• New policies, operating procedures, decision rules, information flows,

organizational procedures, etc. can be explored without disrupting ongoing operation of the real system

• New hardware design, physical layouts, transportation systems, etc. can be tested without committing resources for their acquisitions

• Hypotheses about how and why certain phenomena occur can be tested• Time can be compressed or expanded allowing for a speedup or slowdown of the

phenomena under investigation• Bottleneck analysis can be performed indicating where work‐in‐process,

information, materials, and so on are being excessively delayed• “What‐if” questions can be answered. This is particularly useful in the design of

new systems


Disadvantages of Simulation• Model building requires special training

– Solution: vendor simulation software have been developing packages that contain all or part of models that need only input data for their operation.

• Simulation results may be difficult to interpret– Solution :Many simulation software vendors have developed output analysis

capabilities for performing very through analysis.• Simulation modeling and analysis can be time consuming and expensive

– Solution : Simulation can be performed faster today than yesterday, and even faster tomorrow. This is attribute to the advances in many simulation packages. For example, some simulation software contains constructions for modeling material handling using transporters such as fork lifts, conveyors, AGVs, and others.


Application Area in Logistics and Production

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Logistics simulation Distribution simulationTransports simulation

Warehouse simulation Manufacturing simulation Handling Systems simulation


Simulation in logisticsThe simulation models we are going to study in the rest of this lecture will be discrete, dynamic, and stochastic and therefore will be called discrete‐event simulationmodels.

System Model

Dynamic

Stochastic

Discrete

Deterministic

Static Dynamic

continuous Discrete continuous

Static

Discrete‐event simulation

Concepts in Discrete‐Event Simulation

• System: a collection of entities (e.g. people, machines) that interact together over time to accomplish one or more goals.

• Model: an abstraction representation of a system, usually containing structural, logical, or mathematical relationships which describe a system in terms of state, entities and their attributes, sets, processes, events, activities, and delays.

• System state: A collection of variables that contain all the information necessary to describe the system at any time.

• Entity: any object or component in the system which requires explicit representation in the model (e.g., a server, a customer, a machine)

• Attributes: the property of a given entity ( e.g, the priority of a waiting customer)• Activity: a duration of time of specified length (e.g., a service time or interarrival

time), which is known when it begins ( it may be defined in terms of a statistical distribution)

• Event: an instantaneous occurrence that changes the state of a system (such as an arrival of a new customer)


Concepts in Discrete‐Event Simulation

• List: A collection of (permanently or temporarily) associated entities, ordered in some logical fashion ( such as all customers currently in a waiting line, ordered by FCFS, or by priority)

• Event notice: A record of an event to occur at the current or some future time, along with any associated data necessary to execute the event; at a minimum, the record includes the event type and the event time.

• Event list: A list of event notices for future events, ordered by time of occurrence; also known as the future event list (FEL)

• Delay: A duration of time of unspecified indefinite length, which is not known until it ends (e.g. a customer’s delay in a last‐in, first‐out waiting line which, when it begins, depends on future arrivals)


Examples of Systems and Components

System Entities Attributes Activities Events State Variables

Banking Customers

Checking accountsbalance

Making deposits

Arrivals;Departure

Number of busy tellers; number of customers

waiting

Rapid Rail Riders Origination;destination Traveling

Arrival at station;Arrival at destination

Number of riders waiting at each station; number of riders in

transit

Production Machines

Speed; capacity;Breakdown

rate

Welding;Stamping Breakdown Status of machines

(busy, idle or down)

Inventory Warehouse Capacity Withdra

wing Demand Levels of inventory; backlogged demands


The Able Baker Carhop Problem



Simulation of Inventor System• An important class of simulation problems involves inventory system. A simple

inventory system is show below:

An (M,N) inventory system, with lead time is zero.


N N N

TTime

I

M

Am

ount

in in

vent

ory

Q1Q2

Q3

Simulation of Reliability• a large wind turbine with three blades that fail in

service. The cumulative distribution function of the life of each blade is identical.

• The delay time of the repairman’s arriving at the turbine is random. Downtime for the turbine is estimated at €500/m, the direct on‐site cost of the repairman is €100/h, it takes 60 minutes to change one blades, 100 minutes to change two, and 120 minutes to change all three blades. Each blades cost €1,000. A proposal has been made to replace all three bearings whenever one blade fails. It needs to be evaluated of this proposal.


12.04.2011 21Rechnergestützte Modellierung

Zukunft der Fabriken –Planung von Fabriken

„Lange bevor ein neues Werk entsteht, simulieren Siemens‐Experten die Fabrik im Computer. Diese 3D‐Modelle enthalten Tausende von Parametern und berechnen daraus zum Beispiel die optimale Anordnung der Maschinen, den Transportweg von Bauteilen, die Risiken einer Standortverlagerung oder sogar die Rückenbelastung eines Arbeiters.“

‐‐‐ Siemens A&D

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Simulation in der Logistik - uni-due.de · PDF filetu. l. Slide 1. Simulation with Arena. Prof. Dr.-Ing. Bernd Noche. Department of Engineering Sciences. Division of Mechanical Engineering