Maste Thesisgid

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tul

Fakultt fr Ingenieurwissenschaften

Abteilung Maschinenbau

Transportsysteme und -logistik

Lotharstrae 1 - 21

47057 Duisburg

Prof.Dr.-Ing. Bernd Noche

Telefon: 0203 379-2785

Telefax: 0203 379-3048

E-Mail: [email protected]

Mathematical Optimisation for

Supply Chain and Network Design for

Multi Level Multi Items

Master Thesis

By

Mandar P. Jawale

University of Duisburg-Essen

Institute for Product Engineering

Transport Systems and Logistics

Prof. Dr.-Ing B. Noche

Supervisor

Prof. Dr.-Ing Bernd Noche

M.Sc. Fathi A. Rhoma


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Mathematical Optimisation for Supply Chain and Network Design for Multi Level Multi Items

ii

Acknowledgement

A special thanks to Prof.-Ing Dr. Bernd Noche for his advice and supervision which made me

realize this thesis. It has always been a delight to work under his aegis because of the produc-

tive knowledge sharing sessions and his support. His ideas and concepts comments have been

important support throughout this work.

I would also like to thank Mr. Fathi Rhoma for the technical guidance which he gave me during

my entire Master thesis study. His extensive discussions around my work and interesting explo-

rations have been very helpful for this thesis. Mr. Rhomas essential assistance in reviewing the

thesis accompanied with detailed review and excellent advices during the preparation have pro-

vided a good basis for the presentation of the thesis.


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Abstract

The management of solid waste has received wide attention from economic, environmental and

academic because of the complex nature of these services. Technical and economic problemsemerge in parts because of rising demand which has been resulted due to income, population

growth, a rising level of urbanization, and decline of suitable disposal sites. These problems

challenge researchers to search for more efficient solid waste management methods.

This thesis deals with the development and application of a Logistic concept model for the op-

timal operations, capacity expansions and locations of solid waste facilities. To achieve this goal

a mathematical model is presented in this thesis. The economical and environmental aspects are

considered for selecting strategies that minimize the cost of waste collection, transportation,

operation, and disposal, subject to physical constraints.

A mixed integer programming model is proposed for multiple type waste generated acrossdistributed waste generation points; transported to multi facilities (treatment plants) sharing

similar transfer stations. The proposed model is applied to one of the most densely populated

city in Germany.


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Index

Abstract ..................................... ............................................ ........................................... ........... iii

List of Figures ...................................... ........................................... ............................................ vi

List of Tables ............................................................................................................................. vii

Abbreviations ........................................................................................................................... viii

1

Introduction........................................ ........................................... ................................... 1

1.1 Thesis Objective ........................... ....................................... ........................................... ... 1

1.2

Background ....................................... .......................................... ....................................... 1

1.3

From waste to an economic force ............................................ .......................................... 2

1.4 Literature survey ...................................... ........................................... ............................... 2

2

Waste Management .............................. ............................................ ............................... 4

2.1 Definition of waste ........ ........................................... ............................................ ............. 4

2.2

Structure of the European Waste list .............................................. ................................... 5

2.3

Classification rules ...................................... ........................................ ............................... 6

2.4 Waste classification list ............ ........................................... ........................................... ... 7

2.4.1 Waste Codes for the different Waste .................................... ..................................... 8

3

Network Design .................................................... ............................................... ........... 10

3.1

Network design in supply chain ............................................... ........................................ 10

3.2 The Role of Network Design for Facilities Location in Logistics System ...................... 11

3.3 Factor Influencing Network Design Decisions ...................................... .......................... 11

3.4 A Framework for Network Design Decisions ................................................................. 13

3.5 Model for Facility Location and Capacity Allocation .................................................... . 14

4 Transfer Stations ..................................... ......................................... ............................. 16

4.1 What Are Waste Transfer Stations? ......................................... ........................................ 16

4.2 Site Selection ................................ ........................................... ........................................ 17

4.3 Determining Transfer Station Size and Capacity .......................................... ................... 18

4.4 Transfer Station Design .............................................................. ..................................... 18

4.5

Basic Transfer Station Technologies and Operations ...................................................... 19

5

Inland waterways transportation .................................. ........................................... .... 20

5.1 Traffic volumes and forecasts ................................................. ......................................... 20

5.2 Energy requirement .................. ........................................... ........................................... . 21

5.3 Traffic Safety .......................................... ........................................... .............................. 21

5.4 Traffic Noise ..................................... .......................................... ..................................... 21


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5.5 Emissions .................................. ........................................... ............................................ 22

5.6 Multiple uses of the waterways ......................... ........................................... ................... 22

6 Case study - City Duisburg .......................................................... ................................. 24

6.1 Mathematical formulation................................................................................................ 26

7

Scenarios ......................................... ............................................. ................................... 30

7.1 Scenario 1: Waste transported direct to waste treatment plants....................................... 31

7.2

Scenario 2: Waste transported through transfer stations. ......................................... ........ 32

7.3

Scenario 3: Waste transported through ports (waterways). ......................................... .... 33

7.4 Scenario 4: Waste transportation by port and transfer station. ........................................ 35

7.5 Scenario 5: ............................. .............................................. ............................................ 37

8 Results and Analysis ......................................................................... ............................. 38

9

Conclusion and Future Work .......................................................... ............................. 50

Appendix A: Bibliography ..................... ........................................... ........................................ 51

Appendix B: Basic Input Data ............................................................... ................................... 53

Appendix C: Output Data ...................... ........................................... ........................................ 58


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List of Figures

Figure 1: Waste Flow ............................................................................ ......................................... 4

Figure 2: Hazardous waste flow chart ........................................... ........................................... ...... 5Figure 3 Coding System .......................................... ........................................ ............................... 5

Figure 4 Flow diagrams to identify correct waste category .......................................................... 7

Figure 5: Global Network Design Decisions ............................................................ ................... 13

Figure 6: Comparison between Transfer station concept and direct transportation ..................... 17

Figure 7: Actual calculations of energy consumption (mega Joule per ton) for eight selectedbulk transport ...................................... .......................................... .................................. 21

Figure 8: Comparision for Noise cost (cent per ton) with different modes of transport .............. 22

Figure 9: Comparision of emissions (cents per ton-km) for different modes of transport. .......... 22

Figure 10: Transportation Cost ( per ton) for bulk transportation. ..................................... ........ 23

Figure 11: Transportation Cost ( per ton) for Container transportation ..................................... 23

Figure 12: The flow chart of the mathematical formulation ........................................................ 28

Figure 13: Mathematical calculations using Lingo .............................................................. ........ 29

Figure 14: Lingo interface: (A) code for model (B) Solution from the solver ............................. 29

Figure 15: Scenario 1 ......................................................... ........................................... ............... 31

Figure 16: Scenario 2 ......................................................... ........................................... ............... 32

Figure 17: Scenario 3 ......................................................... ........................................... ............... 33

Figure 18: Scenario 4 ......................................................... ........................................... ............... 35

Figure 19: Scenario 5 ......................................................... ........................................... ............... 37

Figure 21: Total cost and Distance for Scenarios ................. ............................................ ........... 40

Figure 22: CO2 emission for various scenarios ..................................................... ...................... 41

Figure 23: Waste source points in Duisburg ................................................................. ............... 42

Figure 24: Total distance travelled (km) (including empty trip+full load) .................................. 43

Figure 25: Cost analysis w.r.t distance and cost of transportation of waste for directtransport to Incineration plant and Transportation through transfer station toincineration plant. ....................................... ............................................... ...................... 45

Figure 26: Unit waste transported per km for direct transport to Incineration plant andTransportation through transfer station to incineration plant. ......................................... 47

Figure 27: Source point where waste is directly transported to incineration plant ...................... 47

Figure 28: Cost analysis w.r.t distance and cost of transportation of waste for directtransport to Incineration plant and Transportation through Port to incinerationplant. ...................................... ........................................... ........................................... .... 48

Figure 29: Unit waste transported per km for direct transport to Incineration plant andTransportation through transfer station to incineration plant. ......................................... 49


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List of Tables

Table 1: Freight transportation by modes in Germany 2004 .................................... ................... 20

Table 2: Forecast for inland shipping................................. ....................................... ................... 20Table 3: The mean region in the City with waste quantity per week ........................................... 24

Table 4: Scenarios with combinations of waste and transfer stations. ........................................ . 30

Table 5: Waste allocation for Scenario 2 ............................................................... ...................... 38

Figure 20: Location of the ports ......................................... ........................................... ............... 39



Table 8: Total distance travelled (km) (including empty trip+full load) .................. ................... 42

Table 9: Cost for Scenarios ......................................... ........................................... ...................... 43

Table 10: Waste transported per km .................................................... ........................................ 43

Table 11: Empty distance travelled ...................................... ........................................... ............. 44

Table 12: Sample calculations ..................................................................................................... 44

Table 13: Average distance between Source point and Incineration Plant .................................. 46

Table 14: Division of City-Duisburg (region wise) ........................................ ............................. 53

Table 15: Location of Garages, Transfer-station and Ports ............. ..................................... ....... 53

Table 16: Waste handling treatment plants ............................................................ ...................... 54

Table 17: Charges for transportation and other services .................................... .......................... 54

Table 18: CO2 emissions for vehicles ...... ...................................... ..................................... ........ 54

Table 19: Waste quantity generated in each area (tons per two weeks)....................................... 55

Table 20: Distance between waste generation source and Garage, Transfer-stations (km). ........ 56

Table 21: Distance between waste generation source and ports, waste handling plants (km) ..... 57

Table 22: Waste allocation for Scenario 1 ......................................................... .......................... 58

Table 23: Distance, transportation cost and total cost for Scenario 1 .................................. ........ 59


Table 25: Waste distribution for Scenario 2 ........... ........................................... .......................... 61

Table 26: Distance, transportation cost and Total cost for Scenario 2 ........................................ . 61



Table 29: Distances, transportation cost and Total cost for Scenario 3 ..................................... .. 63

Table 30: Waste Allocation for Scenario 4 ....................................................................... ........... 64


Table 32: Distances, transportation cost and Total cost for Scenario 4 ...................................... . 65

Table 33: CO2 emissions ................................. ........................................... ................................. 65

Table 34: Direct transportation to the Incineration plant (quantity, distance and total cost) ....... 66

Table 35: Transportation to the Incineration plant through Transfer stations (quantity,distance and total cost) ..................................................... ....................................... ........ 67

Table 36: Transportation to the Incineration plant through Port (quantity, distance and totalcost) ....................................................................... ........................................... ............... 68


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Abbreviations

G1 Garage 1

HF1 Hafen 1

MRF Material Recycling Facility

MSW Municipal Solid Waste management

SWM Solid Waste Management

TS Transfer Station

WH Waste Handling facility


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1 Introduction

1.1

Thesis Objective

This thesis will focus on the allocation of waste supply chain. The location of transfer station,

transfer station capacity and difference size of collection vehicle are also considered in this the-

sis. This thesis investigates the municipal waste starting from the waste generation point

(household) till the municipal wastes at different waste disposal plants. To design a supply chain

of waste collection system from the logistics point of view, this thesis proposes transfer stations

or transportation through inland waterways in order to reduce the logistics costs including trans-

portation costs, location costs and also minimizing the CO2 emissions.

The objective of this thesis is to minimize total logistics cost by applying mathematical model

for municipal waste collection system with using the advantage of transfer station and inland

port available for waste collection system. This will give the minimum total cost of waste allo-

cation in logistics supply chain without ignoring the environmental aspect by minimizing the

CO2 emission.

1.2 Background

The management of solid waste has become a significant research problem. And so there is a

need to take a leap in terms of efficiently using resources and energy that how waste become

our energy. The waste industry in Germany has a key role to play in that. Since the first law onwaste management came into force in Germany in 1972, waste policy has achieved a great deal.

Whilst in the past, waste was simply dumped in landfills, today there is a very high-tech and

specialized closed substance cycle. Innovative processes and technologies allow us to fully and

efficiently recycle our waste, turning todays trash into tomorrows treasure-trove.

Environmental and social issues emerge as people become increasingly concerned about the

risks associated with living close to solid waste facilities. For example, many suitable sites and

disposal locations have been investigated for disposing and incinerating the solid waste. Some

studies also have shown that landfill disposal decreases neighbouring property values. There-

fore, residents oppose to establish new facilities.

The Ordinance on Environmentally Compatible Storage of Waste from Human Settlements and

on Biological Waste Treatment Facilities represents a milestone in that respect. Since 1 June

2005 waste may no longer be dumped in landfills without any pre-treatment, putting an end to

storage that is detrimental to the environment. The closed substance cycle is a good example of

how environmental policy contributes to more environmental protection, efficient use of re-


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sources, climate protection and thus also to more economic efficiency. That creates competi-

tive advantages for the economy as a whole in other ways too.

Today, the waste industry employs over 250,000 people and generates an annual turnover of 50

billion Euros. Therefore, substance network design of solid waste collection management and

operation is our goal.

1.3 From waste to an economic force

Waste management has evolved substantially since the early 1970s: Before the Waste Manage-

ment Act of 1972 came into effect, each village and town had its own tip (around 50,000 in the

whole of Germany). In the 1980s and 1990s their number dropped to below 2,000, whilst at the

same time strict regulations were introduced regarding their construction and operation. Today

only 160 landfill sites in Germany handle municipal waste (Class II landfill sites). The number

of incineration plants, municipal waste facilities and plants for industrial waste has, by contrast,

increased significantly. In the mid-1980s the political credo of the so-called waste hierarchy

avoid - reuse - dispose of gained acceptance. In addition to the existing recovery of metal,

textiles and paper, other recoverable materials were to be recycled by means of separate collec-

tion, sorting and reuse. This rationale formed the basis for the Closed Substance Cycle and

Waste Management Act which came into force in the mid-1990s. Today, the waste industry in

Germany employs more than 250,000 people from engineers to refuse collectors to adminis-

trative staff. Various universities have Waste Management faculties, and there is a separate vo-

cational qualification in waste disposal. The industry generates an annual turnover in excess of

50 billion Euros. Today, much more than half of municipal and production waste is recycled. In

some areas, for example packaging, around 80% is recycled. 87% of construction waste is now

recovered. Figures for the total volumes of waste recovered make impressive reading: 29 mil-lion tonnes of municipal waste, 31 million tonnes of production and industrial waste, and 161

million tonnes of construction and demolition waste. Around four tones of waste are recovered

for each resident in Germany, thats nearly equivalent to the weight of four small cars. These

figures provide impressive proof that environmental protection has developed into a key eco-

nomic factor, making a significant contribution to an economys value added chain.

1.4 Literature survey

Many research centre and experts have investigated the solid waste management problem. In

this section will present the earlier different solid waste management models in the last 20 years.

The waste management model first handles locating intermediate point transfer station, treat-

ment, recycling and landfill location problem. There is a significant amount of literature on un-

desirable facility locations for more information check Erkut and Neuman [EN 1989].

There are other studies in the literature that are only concerned with the routing aspect of the

Solid waste management problem. These kind of studies attempt to find optimal route for col-


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lect the solid waste from the generation point for door to door that minimize the distance travel

and the total costs.

The effective application of SWM mathematical integrated models as tools for decision made

by municipal solid waste planners, in developing countries, is still a big challenge. A consider-

able amount of research has been done in the last two decades on various aspects of SWM, and

a number of economically based optimization models for waste streams allocation and collec-

tion vehicle routes, have been developed. Owing to an increasing awareness of environmental

protection and conservation of natural resources, rising prices of raw materials, and energy con-

servation concerns, the current research in SWM is now guided by the aim of designing com-

prehensive models that take into account multi-disciplinary aspects involving economic, techni-

cal, regulatory, and environmental sustainability issues.

The solid waste models that have been developed in the last two decades have varied in goals

and methodologies. Solid waste generation prediction, facility site selection, facility capacity

expansion, facility operation, vehicle routing, system scheduling, waste flow and overall system

operation, have been some of these goals Badran and El-Haggar [BES 2006].

Some of the techniques that have been used include linear programming, integer programming,

mixed integer programming, non-linear programming, dynamic programming, goal program-

ming, grey programming, fuzzy programming, quadratic programming, and stochastic pro-

gramming, two stage programming, and interval-parameter programming, geographic informa-

tion systems. Helms and Clark [HC 1971] used linear programming to select solid waste disposal

facilities among various proposed alternative sites. Esmaili [Es 1973] presented simulation model

approach to compute the cost of different combinations of facilities , including facility sitting

and expansion over time , Kaila [K 1987] used dynamic programming with a heuristic approach

to evaluate waste management systems that include more than one facility option to find out the

least cost alternative concerning the collection , transportation , processing , and disposal activi-

ties.

Gottinger [Go 1986] developed a model where potential management facilities are given. The

model minimizes the total cost, which includes fix and variable facility costs, and transportation

costs. To determine the number of facilities needed, facilities location, how to route the collec-

tion vehicle and how to process and dispose these facilities; Huang et al. 1995 developed grey

integer programming models to solve the problem of waste management planning under uncer-

tainty, particularly uncertainty related to the environment and the economy. The object of the

model was to identify an optimal facility expansion plan and municipal solid waste flow alloca-

tion presented a model to optimize disposal costs by trading off transportation costs against thecapital costs of introducing the transfer stations. Komilis developed two conceptual mixed inte-

ger liner optimization models to optimize the haul and transfer to Municipal solid waste. Chang

[CW 1996] extends the facility site model. This model differs from prior work by its considera-

tion of environmental impacts; such as not only determines the location and capacity of solid

waste facilities, but also the level of facilities operation over time.


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2

Waste Management

2.1

Definition of waste

Waste Management Acts 1996 and 2001[EWC 2002]

Waste is defined in Section 4(1) of the Waste Management Acts 1996 and 2001 as any sub-

stance or object belonging to a category of waste specified in the First Schedule [of the Waste

Management Act] or for the time being included in the European Waste Catalogue which the

holder discards or intends or is required to discard, and anything which is discarded or otherwise

dealt with as if it were waste shall be presumed to be waste until the contrary is proved.

Figure 1: Waste Flow

The next step in the waste is the identifying the given waste as hazardous or non hazardous. The

following flow diagram will show the basic step in classifying the waste as hazardous according

to the European waste catalogue.


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Figure 2: Hazardous waste flow chart

2.2 Structure of the European Waste list

The European Waste List includes 839 types of waste. Of these, 405 are waste catego-ries for

hazardous waste and are marked with a star *[EWC 2002]. The 839 types of waste are di-vided

into 20 chapters. Each of the 20 chapters represents either an industrial or com-mercial activity(chapters 1 to 12 and 17 to 19) or an industrial process (chapters 6 and 7) or a specific material

(chapters 13 to 15). Chapter 20 contains municipal waste. Chapter 16 is miscellaneous waste

which has not been allocated to other chapters. The chapters are further divided into sub-

chapters. This sub-division varies: chapter 9, for example, contains only one sub-chapter, chap-

ter 10 on the other hand is further divided into 14 sub-chapters.

Figure 3 Coding System

A six-digit decimal classification system, XX YY ZZ, is used in the European Waste List for

coding. XX stands for 01 to 20 for the 20 chapters. YY is the grouping where YY = 01 to

maximal 14 and under ZZ, the types of waste with 01 ff are listed. In addition, a range of sub-


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chapters includes a waste code that is identified with the decimal point ZZ = 99 which includes

miscellaneous wastes in that specific sub-chapter.

2.3 Classification rules

i. If a specific waste is to be identified in a waste category, this must be done in accor-dance with No.3 of the introduction to the Commissions decision 2001/118/EC as fol-

lows (see fig. 3):

ii. Identify the field of activity to which the waste producer belongs, i.e. chapters 1 to 12 or

17 to 20.

iii. Identify the sub-chapter within the chapter which best characterises the source of the

waste.

iv. Within the sub-chapter, identify the waste category which best characterises the waste.

The specific is always to be identified over the general.

v. If no appropriate waste category can be found in chapters 01 to 12 or 17 to 20, chapters

13, 14 and 15 should be examined as described above in steps 2 and 3 be-fore resorting

to waste categories XX YY 99.

vi. If only one waste category XX YY 99 comes into question, the waste should be identi-

fied with a waste category in chapter 16, in accordance with steps 2 and 3 above.

vii. If a suitable waste category cannot be found in chapter 16, then XX YY 99 is to be used

in the chapter and sub-chapter corresponding to the most appropriate source producing

the waste.


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Figure 4 Flow diagrams to identify correct waste category

2.4 Waste classification list

The following shows the major classification categories for waste [EWC 2002]:

01 Wastes resulting from exploration, mining, quarrying, physical and chemical

treatment of minerals.

02 Wastes from agriculture, horticulture, aquaculture, forestry, hunting and fishing,

food preparation and processing.

03 Wastes from wood processing and the production of panels and furniture, pulp,

paper and cardboard.

04 Wastes from the leather, fur and textile industries.

05 Wastes from petroleum refining, natural gas purification and pyrolytic treatment of

coal.

06 Wastes from inorganic chemical processes.

07 Wastes from organic chemical processes.

08 Wastes from the manufacture, formulation, supply and use (MFSU) of coatings

(paints, varnishes and vitreous enamels), sealants and printing inks

09 Wastes from photographic industry.

10 Wastes from thermal processes.

11 Wastes from chemical surface treatment and coating of metals and other materials;

non-ferrous hydro-metallurgy.


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12 Wastes from shaping and physical and mechanical surface treatment of metals and

plastics.

13 Oil wastes and wastes of liquid fuels (except edible oils, 05 and 12).

14 Waste organic solvents, refrigerants and propellants (except 07 and 08).

15 Waste packaging; absorbents, wiping cloths, filter materials and protective clothingnot otherwise specified.

16 Wastes not otherwise specified in the list.

17 Construction and demolition wastes (including excavated soil from contaminated

sites).

18 Wastes from human or animal health care and/or related research (except kitchen

and restaurant wastes not arising from immediate health care).

19 Wastes from waste management facilities, off-site waste water treatment plants and

the preparation of water intended for human consumption and water for industrialuse.

20 Municipal wastes (household waste and similar commercial, industrial and

institutional wastes) including separately collected fractions.

2.4.1 Waste Codes for the different Waste

The following are the waste codes as defined by the EWC, for the municipal waste that is de-

scribed in this thesis. The detailed breakdowns of the codes are as follows:

Paper

20 01 01 paper and cardboard

19 12 01 paper and cardboard

15 01 01 paper and cardboard packaging

Bio-waste

02 02 wastes from the preparation and processing of meat, fish and other foods of animal

origin

02 02 01 sludges from washing and cleaning

02 02 02 animal-tissue waste

02 02 03 materials unsuitable for consumption or processing

20 01 08 biodegradable kitchen and canteen waste

20 02 garden and park wastes (including cemetery waste)

20 02 01 biodegradable waste


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Plastic and Packaging (Gelbe)

20 01 39 plastics

15 01 02 plastic packaging

15 01 05 composite packaging

15 01 06 mixed packaging

Restmll

20 01 37* wood containing dangerous substances

20 01 38 wood other than that mentioned in 20 01 37

20 01 39 plastics

20 01 40 metals

20 02 03 other non-biodegradable wastes

20 03 07 bulky waste

20 03 99 municipal wastes not otherwise specified

20 01 35* discarded electrical and electronic equipment other than those mentioned in 20 01

21 and

20 01 23 containing hazardous components.

20 01 36 discarded electrical and electronic equipment other than those mentioned in 20 01

21, 20 01 23 and 20 01 35


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3

Network Design

3.1

Network design in supply chain

Network design refers to the step taken to move and store a product from supplier stage to a

customer stage in the supply chain. Network occurs between every pair of stage in supply chain.

Raw materials and components are moved from supplier to manufacturers, whereas finished

product and moved from the manufacturer to the end consumer. Network design is a key driver

of the overall profitability of a firm because it directly impact both in supply chain cost and the

customer experience. For example, distribution related costs from about 10.5 percent of the U.S.

economy and about 20 percent of the cost of manufacturing. For commodity products, distribu-

tion forms an even higher fraction of the product cost. In India, the outbound distribution cost of

cement is about 30 percent of the cost of producing and selling cement.

The choice of the network design can be used to achieve a variety of supply chain objectives

ranging from the low cost to high responsiveness. As a result, companies in the same industry

often select very different network. Next, discuss the example of network design of different

companies to the highlight the variety of distribution choice and the issues that arise when se-

lecting among this options.

In Dell distribution, its PCs directly to end customers, whereas companies like HP distribute

through resellers. Dell customers wait several days to get a PC while customers can walk away

with as HP PC from reseller. Gateway opened Gateway Country stores where customers could

check out the products and have sales people help them to configure a PC that suited theirneeds. Gateway, however, chose to sell no products at stores, with all PCs shipped directly from

the factory to the customers. In 2001, Gateway closed several of these stores given the poor

financial performance. Apple computer is planning to open retail stores where computer will be

sold. These PC companies have chosen three different network designs. How can we evaluate

this wide range of network design? Which ones serve the companies and their customer better?

P&G has chosen the network that distribute directly to large supermarket chains while making

the smaller players buy P&G from the retailer. The product moves faster from P&G to the larger

chains while moving through an additional stage then going to the smaller supermarkets. Texas

Instruments, which once used only sales, now sells about 30 percent of its volume to 98 percent

of its customers through retailer, while serving the remaining 2 percent of customers with 70

percent of the volume directly [CPE 2007].


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3.2 The Role of Network Design for Facilities Location in Logistics

System

Supply chain network design decisions include the location of manufacturing storage or trans-

portation-related facilities and the allocation of capacity and roles to each facility. Supply chain

network design decisions are classified as follow:

1. Facility role: What role should each facility play? What processes are performed at each

facility?

2. Facility location: Where should facility be located?

3. Capacity allocation: How much capacity should be allocated to each facility?

4. Market and supply allocation: What markets should each facility serve? Which supply

source should feed each facility?

Facility location decisions have a long-term impact on a supply chains performance because it

is very expensive to shut down a facility or move it to a different location. A good location deci-sion help a supply chain be responsive while keeping its costs low.

In contrast, poorly located facility makes it very difficult for a supply chain to perform close to

the efficient frontier.

Capacity allocation decisions also have a significant impact on supply chain performance.

Whereas capacity allocation can be altered more easily than location, capacity decisions do tend

to stay in place for several years. Allocating too much capacity to a location results in poor re-

sponsiveness if demand is not satisfied or high cost of demand is filled from a distance facility.

The allocation of supply sources and markets to facilities has a significant impact or perform-

ance because it affects total production, inventory and transportation costs incurred by the sup-ply chain to satisfy customer demand.

Network design decisions are also very important when two companies merge. Due to the re-

dundancies and differences in markets served by either of the two separate firms, consolidating

some facilities and changing the location and role of others can often help reduce cost and im-

prove responsiveness.

3.3 Factor Influencing Network Design Decisions

Strategic, technology, macroeconomic, political, infrastructure, competitive, and operationalfactors influence network design decisions in supply chain.

Strategic factors

A firms competitive strategy has a significant impact on network design decisions within the

supply chain. Firms focusing of cost leadership tend to find the lowest cost location for their

manufacturing facilities, even of that means locating very far from the market they serve. Global


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supply chain networks can be best support their strategic objectives with facilities in different

countries playing different roles.

It is important for a firm to identify the mission or strategic role of each facility when designing

its global network. Kasra Ferdows (1997) suggests the follow classification of possible strategic

roles for various facilities in a global supply chain network1.

1. Offshore Facility: Low-cost facility for export production.

2. Source Facility: Low-cost facility for global production.

3. Server Facility: Regional production facility.

4. Contributor Facility: Regional production facility with development skills.

5. Output Facility: Regional production facility built to gain local skills.

6. Lead Facility: Facility that leads in development and process technologies.

Technological Factors

Flexibility of the production technology impacts the degree of consolidation that can be

achieved in the network. If the production technology is very inflexible and product requirement

far from one country to another, a firm has to set up local facilities to serve the market in each

country.

Infrastructure Factors

The availability of good infrastructure is an important prerequisite to locating a facility in a

given area. Poor infrastructure adds to the cost of doing business from a given location.

Global companies have located their facilities in China near Shanghai, Tianjin, or Guangzhou,

even though these locations do not have the lowest labour or land cost because of better infra-structure at these locations. Key infrastructure elements to be considered during network design

include availability of sites, labour availability, proximity to transportation terminals, rail ser-

vice, proximity to airport and seaport, high way access, congestion, and local utilities.

Logistics and Facilities costs

Logistics and facilities costs incurred within a supply chain change as the number of facilities,

their location, and capacity allocation is changed. Companies must consider inventory, transpor-

tation, and facility cost when designing their supply chain networks.

Inventory and facility costs increase as the number of facilities in a supply chain increase.

Transportation costs decrease as the number of facilities is increased. Increasing the number of

facilities to a point where inbound economies of scale are lost increases transportation cost.

The supply chain network design is also influenced by the transformation occurring at each

facility. When there is a significant reduction in material weight or volume as a result of proc-

essing, it may be better to locate facilities closer to the supply source rather than the customer.

For example, when iron ore is processed to make steel, the amount of output is small fraction of


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the amount of ore used. Locating the steel factory close to supply source is preferred because it

reduces the distance that the large quantity of ore has to travel.

3.4 A Framework for Network Design Decisions

When faced with a network design decision, the goal of a manager is to design a network thatmaximizes the firms profits while satisfying customer needs in terms of demand and respon-

siveness. To design an effective network manager must consider the entire factor. Global net-

work design decisions are made in four phases as shown in figure. Each phase is described in

greater detail.

Internal Constraints

Capital, growth strategy, existing

network

Competitive Strategy

Production Technologies

Cost, scale/scope impact, support

required, flexibility

Global Competition

Competitive Environment

Regional Demand

Size, growth, homogeneity, local

specification

Production Methods

Skill needs, response time

Tariffs and tax incentives

Factor CostsLabor, materials, site specific

Political, Exchange rate, and

Demand Risk

Available Infrastructure

Logistics Costs

Transport, inventory, coordination

Phase I

Supply chain strategy

Phase III

Desirable sites

Phase II

Regional facility configuration

Phase IV

Location choices

Figure 5: Global Network Design Decisions

Phase II: Define the Regional Facility Configuration

The objective of the second phase of network design is to identify regions where facilities will

be located their potential roles, and approximate capacity.

An analysis of Phase II is started with a forecast of the demand by country. Such a forecast must

include a measure of the size of the demand as well as determination of whether the customerrequirements are homogenous or variable across different countries. Homogenous requirements

favour large consolidated facilities whereas requirements that vary across countries favour

smaller, localized facilities.

The next step for managers is to identify whether economics of scale or scope can play a sig-

nificant role in reducing costs given available production technologies. If economies of scale or


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5. Transportation costs between each pair of sites

6. Inventory costs by site as well as a function of quantity

7. Sale price of product in different regions

8. Taxes and tariffs as product is moved between locations

9. Desired response time and other service factors

Given this information, either gravity or network optimization models may be used to design the

network. We organize the model according to the phase of the network design framework where

each model is likely to be useful [CPE 2007].

Network Optimization Models

During Phase II of the network design framework, a manager must consider regional demand,

tariffs, economies of scale, and aggregate factor costs to decide the regions in which facilities

are to be located. The disadvantage of this approach is that plants will be sized only to meet

local demand and may not fully exploit economies of scale. During phase IV, a manager mustdecide on the location and capacity allocation for each facility. Besides locating the facilities, a

manager must also decide how markets will be allocated to facilities. This allocation must ac-

count for customer service constraints in term of response time. The demand allocation decision

can be altered on a regular basis as costs change and markets evolve. When designing the net-

work, both location and allocation decision are made jointly. Network optimization model are

critical tools for both the network design and demand allocation decisions [CPE 2007].


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pared for shipment to markets in a special facility called a MRF, which stands for materials

recovery facility. A MRF is simply a special type of transfer station that separates, processes,

and consolidates recyclable materials for shipment to one or more recovery facilities rather than

a landfill or other disposal site. Consequently, the concepts and practices in this manual can be

applied to MRFs as well. Aggressive community source reduction and recycling programs can

substantially reduce the amount of waste destined for long haul transfer and disposal. If thesereductions are significant enough, a community may find that fewer or smaller transfer stations

can meet its needs [EPA WTM 2002].

Figure 6: Comparison between Transfer station concept and direct transportation

4.2 Site Selection

Identifying a suitable site for a waste transfer station can be a challenging process. Site suitabili-

ty y depends on numerous technical, environmental, economic, social, and political criteria.

When selecting a site, a balance needs to be achieved among the multiple criteria that might

have competing objectives. For example, a site large enough to accommodate all required func-

tions and possibly future expansion might not be centrally located in the area where waste is

generated. Likewise, in densely developed urban areas, ideal sites that include effective natural

buffers simply might not be available. Less than ideal sites may still present the best option dueto transportation, environmental, and economic considerations. Yet another set of issues that

must be addressed relates to public concern or opposition, particularly from people living or

working near the proposed site. The relative weight given to each criterion used in selecting a

suitable site will vary by the communitys needs and concerns. Whether the site is in an urban,

suburban, or rural setting will also play a role in final site selection [EPA WTM 2002].


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4.3 Determining Transfer Station Size and Capacity

The physical size of a planned transfer station is typically determined based on the following

factors:

i. The amount of waste generated within the service area, including projected changes

such as population growth and recycling programs.

ii. The types of vehicles delivering waste (such as car or pickup truck versus a specially

designed waste-hauling truck used by a waste collection company).

iii. The types of materials to be transferred (e.g., compacted versus loose MSW, yard

waste, C&D), including seasonal variations.

iv. Daily and hourly arrival patterns of customers delivering waste. Hourly arrivals tend to

cluster in the middle of the day, with typical peaks just before and after lunchtime. Peak

hourly arrivals tend to dictate a facilitys design more than average daily arrivals.

v. The availability of transfer trailers, intermodal containers, barges, or railcars, and how

fast these can be loaded.

vi. Expected increases in tonnage delivered during the life of the facility. For example, in a

region with annual population growth of 3 to 4 percent, a facility anticipating a 20 year

operating life would typically be designed for about twice the capacity that it uses in its

first year of operation.

vii. The relationship to other existing and proposed solid waste management facilities such

as landfills, recycling facilities, and waste-to-energy facilities [EPA WTM 2002].

4.4

Transfer Station DesignThe most important factors to consider when designing a transfer station are:

i. Will the transfer station receive waste from the general public or limit access to collec-

tion vehicles? If access will not be limited, how will citizen traffic be separated from

commercial traffic to ensure safe and efficient unloading?

ii. What types of waste will the transfer station accept?

iii. What additional functions will be carried out at the transfer station (i.e., material recov-

ery programs, vehicle maintenance)?

iv. What type of transfer technology will be used?

v. How will waste be shipped? Truck, rail, or barge?

vi. What volume of material will the transfer station manage?


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4.5 Basic Transfer Station Technologies and Operations

Waste can be unloaded directly into the open top of the trailer, but is most often unloaded on

the tipping floor to allow for materials recovery and waste inspection before being pushed into

the trailer. Large trailers, usually 100 cubic yards or more, are necessary to get a good payload

because the waste is not compacted. This is a simple technology that does not rely on sophisti-cated equipment (e.g., compactor or baler). Its flexibility makes it the preferred option for low-

volume operations [EPA WTM 2002].

This section describes transfer station operations issues and suggests operational practices in-

tended to minimize the facilitys impact on its host community. Issues covered include:

i. Operations and maintenance plans.

ii. Facility operating hours.

iii. Interacting with the public.

iv. Waste screening.

v. Emergency situations.

vi. Recordkeeping.


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5

Inland waterways transportation

In this chapter, the focus is in on transportation through waterways. Here the comparison of

inland waterway transportation is done with road and rail in Germany. Duisburg makes a pecu-liar case for consideration of intermodal due to it geography and the infrastructure. Well net-

worked with rail and inland shipping its one of the major transportation center, make an ideal

geographic location for the following case study discussed in this thesis [VVBW].

5.1 Traffic volumes and forecasts

In 2004, a total of over 1.5 billion tons of goods German long-distance were transported. Well

15% of this amount of goods was handled by barges. This share should be valued higher as giv-

en that many transport connections are not directly served by barge as these places were not

located on waterways.

Table 1: Freight transportation by modes in Germany 2004

Million (tons) Share (%)

Waterways 235,7 15,6

Railways 310,3 20,5

Roadways 965,7 63,9

With a rise of more than 130 million tons in 2004, inland waterways have assumed importance.

In addition to the bulk (Here the river dominates with a share of 53% of the total volume) con-tainer transport has also profited. Thus, in 2004 nearly 30% of all container shipments to and via

the sea ports were carried (54% trucks, trains 16%). In the future, with further substantial in-

creases in freight transport is expected. Strong growth particularly in international traffic is ex-

pected. For inland shipping, the following are the forecasts:

Table 2: Forecast for inland shipping

Transport service 1997 2015 Increase

Inland shipping 16.3 15.6 -4%

Overall traffic 45.6 74 61%

Total 62.2 89.6 44%

In future there are big potentials in the inland shipping of container transport and also in the

imported coal. For persistent oil price increases inland shipping will be seen as an alternative for

transportation. Other markets with good prospects are the transport of new cars, scrap metal and

heavy [VVBW].


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ping is not considered as noise polluters. The resulting noise costs coming from road transport is

to an average of 0.79 cents and in the rail freight to 0.84 cents per ton. The goods transported by

barges have caused no significant noise costs [VVBW].

Figure 8: Comparision for Noise cost (cent per ton) with different modes of transport

5.5

Emissions

In particular CO2 emissions for the inland shipping are low. The emissions are particularly high

for truck followed by rail. Thus inland shipping proves advantageous when comes to emissions.

Figure 9: Comparision of emissions (cents per ton-km) for different modes of transport.

For the other air pollutants (Carbon monoxide, hydrocarbons, Nitrogen oxides, sulfur dioxide

and particles), the electric rail freight stands at separate when considered in relations to truck

and barge and has a distinct advantages. Due to the expected further development of emissions

of inland there will be tightening of the guidelines for the pollutants in the future. This is similar

to the truck traffic also [VVBW].

5.6 Multiple uses of the waterways

Unlike roads and rails, which have only limited application to perform, inland shipping can

provide multiple options. Constructing roads and laying out rail tracks these processes are some

complex process. The constructions of these are concern of nature. As waterways are natural

existing do not affect nature. For inland shipping, rivers serve out as waterway. Artificial canals


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can be dug out and connected to rivers or each other for networking. Waterways exert a positive

influence on regional development. This results in part from the multiple-use of infrastructure,

water, tourism and leisure and recreation area around the water. Multiple uses of the waterways

exert a positive influence on regional development [VVBW].

Figure 10: Transportation Cost ( per ton) for bulk transportation.

The costs of the trucks are in the average about 50% above those of the train and more than

100% over the inland. The costs of inland are in all cases lower than the train (on average 30%).

Figure 11: Transportation Cost ( per ton) for Container transportation


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6

Case study - City Duisburg

The city of Duisburg comprises an area of 232.81 km2 in Nord-Rhine Westphalia, the most

densely populated state in the Federal Republic. This is the Ruhr region, Germanys industrialheartland. With its 31 power stations, this area is also Germanys major source of energy. In

2008, there were 500914 residents in Duisburg, making it the eleventh largest city in Germany.

Aside from its total population, Duisburg is also one of the countrys most densely populated

cities. With the total number of residential in the City Duisburg were 78775. While the Federal

Republics average population density is 222 inhabitants/km2 and Nord-Rhine Westphalias is

489/km2, Duisburg stands at 2,299 inhabitants/km2, the main residential area are distributed

along the west side of Rhine river [CDW] .

Duisburg is an important transportation centre, with its extensive network of highways and its

access to the Rhine and Ruhr waterways. Indeed, the Rhine-Ruhr port is the largest inland port

in the world. The Duisburg economy was based on manufacturing, with the iron and steel indus-

tries of primary importance, but the micro-electronics sector is rapidly becoming more impor-

tant. Other significant factors in the Duisburg economy are large international trade companies;

a substantial middle class; the service sector; and, as indicated above, the transportation sector.

The regional municipality of City Duisburg includes seven main areas, which were further di-

vided into 46 districts figure 1. The municipality of Duisburg generated 132000 ton/a residual

waste, 38500 ton/a waste paper, 11200 ton/a packing, 38600 ton/a compost waste and 222 ton/a

waste of glasses. As municipal service WBD Wirtschaftsbetriebe Duisburg-AoR [WBR] are

responsible to collected the entire waste daily using different collection vehicle located in two

different depot where the starting in the morning and turn back in the end of the working dayand or the working task , the target of WBR is to provide high quality of customers service at

reasonable prices.

Table 3: The mean region in the City with waste quantity per week

Name Regionspostcode

Population Household

Household waste [Mg/Week]

Population Household

WALSUM 47179 51885 23495 271,314 252,85505

HAMBORN 47168 72591 32786 379,5886 352,84552

MEIDERICH-BEECK 47119 75168 35532 393,0641 382,39819

HOM.-RUHRORTBAERL 47199 42152 20629 220,41877 222,01093

MITTE 47053 106186 58515 555,26161 629,74306

RHEINHAUSEN 47213 79148 37017 413,87608 398,37988

SD 47273 73784 35413 385,82697 381,11751

DUISBURG 500914 243387 2619,35014 2619,35014


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The central vehicle depot is located in the southern part of the city with more than 40 collection

vehicle with different capacity and task, the second depot is located in the northern part of the

city with 25 collection vehicle to service the neighbourhood part of the city.

All the necessary data for the model calculation like facilities location, waste generation quanti-

ties, data for vehicles, labours data and costs data have been obtained from city Duisburg and

the WBD [Wirtschaftsbetriebe Duisburg] the company which is responsible for waste collection

and disposal in city Duisburg. This study does not consider a detail of the waste collection rout-

ing problem , for transportation purposes the waste generated in each of the 46 districts is con-

sidered to exist at a single point within the centre Centroid of the district which is determined

manually. The vehicle transportation distances were determined between each district, transfer

station, waste treatment plants and incineration plant using MapPoint as real distance.


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6.1 Mathematical formulation

The objective of this study is to propose a mathematical model for Municipal solid waste man-

agement of City Duisburg including different scenarios. The proposal model will be minimizing

the total solid waste system costs using mixed integer programming. The best location of thetransfer station from the candidate location list choosing to minimized the total transportation

and operation costs.

Objective function:

Subjected to

(2)(3)(4)

(5)(6)X {0,1} (7)

(8)

Z= total cost of collection, disposal and building facilities;

A= discrete set of different type of waste, A= {1,2,3,4..N}

I=discrete set of source nodes, I={1,2,3,4N};

J=discrete set of transfer station nodes, J={1,2,3,N};

H=discrete set of port nodes, H={1,2,3,N};K=discrete set of treatment facility nodes, K={1,2,3,N};

= units of waste collected from source i and transferred to transfer facility j (per 2 week)

= units of waste collected from source i and transferred to treatment facility k (per 2 week)


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= units of waste collected from transfer facility j and transferred to treatment facility k (per 2

week)

= units of waste collected from source i and transferred to port facility h (per 2 week)

= units of waste collected from port facility h and transferred to treatment facility k (per 2

week)

X = {0,1} integer decision variable, 1 indicating that waste facility has been located and 0 none.

= waste handling Logistics costs/ton (from i to j)

= waste handling Logistics costs/ton (from i to k)

= waste handling Logistics costs/ton (from j to k)

= waste handling Logistics costs/ton (from i to h)

= waste handling Logistics costs/ton (from h to k)

This includes the (collection vehicle operation maintenance cost/ton /mile +labor

cost/ton/mil)*(total trip distance from in km from i to j) +operation and maintenance cost/ton at

facility)

f= amortized weekly fixed cost of building a waste management facility at site j, k( total capital

x capital recovery factor for a design of 20yrs and interest rate of 10% /no. of weeks in a year),

= distance between the nodes

Ts = operation costs for transfer station / ton.

Hs = operation costs for port / ton.wi = waste generated at source node i(tons per 2week)

q = capacity of an SWTS (tons per 2week)


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Figure 12: The flow chart of the mathematical formulation

The objective function is the fixed charge cost function to achieve economic efficiency in locat-

ing transfer points in the collection system. The first three sets of terms in (1) compute the totalcost of short haul collection, direct haul trips, and long haul transfers. The fourth set adds the

amortized capital costs whenever a facility has been located.

The first constraint set (2) represents the service demand constraints. These constraints ensure

that waste wigenerated at each source node i should be shipped out to a transfer facility or port

facility and/or a final waste treatment facility site. Observe that the summation sign for i and j

nodes were defined over full sets I and J. this formulation can be easily modified to define cover

sets on the basis of jurisdictional boundaries or policies. Constraints (3) & (4) represent material

balance equations at each transfer station and ports. This ensures no storage or loss at the trans-

fer site. Constraints set (5) and (6) impose capacity limitations on each transfer station and

ports. The capacity constraints allow various dispersed and site strategies to be analyzed.


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In this thesis Lingo optimization modeling software is used to solve the mathematical expres-

sion along with MS Excel.

Figure 13: Mathematical calculations using Lingo

The data is imported from excel sheets and the mathematical model is run in Lingo. The objec-tive values and the waste allocations are obtained which are exported to Excel. The exported

data is further subjected to calculations and used for graphical representation of results.

Figure 14: Lingo interface: (A) code for model (B) Solution from the solver

A B


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7

Scenarios

For the waste management, models have been created to provide solution to the existing sce-

nario. These models are in combination with concept of intermodality and transfer station.Firstly will start with the basic model, which is current situation. Following combinations of

conditions are applied in the models.

Waste directly send to waste treatment plant.

Waste send through transfer station to the waste treatment plants.

Waste send through port which acts as transfer stations to the waste treatment plants.

Mode of transport and type of vehicle are another factors to be considered. Following are the

combinations:

By truck (10 ton capacity) direct to the waste treatment plant.

By truck (10 ton capacity) first to the transfer station and further transportation by truck

(30 ton capacity) to the waste treatment plant.

By truck (10 ton capacity) first to the transfer station and further transportation by barge

through ports.

The following table will give you the combination of the above stated so as to have clear

idea of the various models that will follow in succeeding chapters.

Table 4: Scenarios with combinations of waste and transfer stations.

Scenario Paper Bio-waste Gelbe waste Restmll

TS1 TS2 TS3 TS4 H1 H2 TS1 TS2 TS3 TS4 TS1 TS2 TS3 TS4 TS1 TS2 TS3 TS4 H1 H2

1

2 x x x x x x x x x x x x x x x x

3 x x x x x x x x x x x x x x

4 x x x x x x x x x x x x x x x x

5 x x x x


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7.1 Scenario 1: Waste transported direct to waste treatment plants.

This is the existing situation for the waste transportation in Duisburg. Here four types of waste

viz. Paper, bio-waste, gelbe and restmll. The trucks start from garage G1 and G2, then head

towards the prescribed 46 waste generation plants across Duisburg. The trucks with 10 ton ca-pacities are used for transportation of waste. This model will be the base model. With help of

this model, other models have been developed. And finally the results from simulation of each

model are compared and to find most feasible model for waste collection supply chain of Duis-

burg.

Figure 15: Scenario 1


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7.2 Scenario 2: Waste transported through transfer stations.

The concept of transfer station is introduced here to improve the MSW collection system for

each type of waste. Here the model works with following possibility:

1. The waste is directly sent to the waste treatment plant.

2. The waste is first sent first to the transfer station where it is compacted then transferred

and through large capacity trucks (30 tons).

This model is first step in reducing the transportation distance. By introduction of the transfer

station, the long trips are reduced. The load carried by truck from transfer station to waste

treatment plant is more due increased capacity of trucks viz. 30 ton load compared 10 ton load

truck for the direct transportation to the waste treatment.



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7.3 Scenario 3: Waste transported through ports (waterways).

In this model the concept of waterways in waste transportation is introduced. The geographical

location of Duisburg makes it possible for use waterway. City Duisburg is located on conflu-

ence of river Rhine and Ruhr. Duisburg being one of major inland port in Europe, so the basicinfrastructure required for a port are readily available. It is observed from the present geo-

graphical location that the restmll treatment plant is located on bank of river Ruhr in Ober-

hausen. The waste can be sent to the incineration plant by ship due presence of ports on either

side. Considering the expansion of the city; strategically two ports in north at Schwelgern and

south at Logport are selected to as to cover up maximum area; which will also serve the purpose

of collecting waste and transporting it by ship to the incineration waste.


All other three wastes are sent by transfer station as stated in above model. Now here the model

works with following possibility:

1. The waste is sent direct to incineration plant.

2. The waste is sent direct to the port first and from there it is transferred to ship and trans-

ported to incineration plant by ship


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7.4 Scenario 4: Waste transportation by port and transfer station.

This model is modified version of the model 3 and model 4. Here combined effect of transfer

station and port together for the restmll is checked. A decision can be made for the restmll

waste to be sent it through transfer station or via port or directly to incineration plant. While forother three waste all other conditions apply same.


So in this model are the following possibilities:

1. The waste is sent direct to incineration plant.

2. All other three wastes viz. Paper, Bio-waste and Gelbe waste are transported through

transfer station with following possibilities:

i. The waste is directly sent to the waste treatment plant.

ii. The waste is first sent first to the transfer station where it is compacted then

transferred and through large capacity trucks (30 tons).


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3. The waste is sent direct to the port first and from there it is transferred to ship and trans-

ported to incineration plant by ship.

4. The waste is first sent first to the transfer station where it is compacted then transferred

and through large capacity trucks (30 tons).


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7.5 Scenario 5:

This is a sub model which will evaluate in detail for using of a transfer station or port for trans-

portation. In this case, waste type restmll is used and following 3 sub model s are created as

follows:

1. Transportation of restmll direct to Incineration plant (existing situation).

2. Transportation of restmll through transfer-station to Incineration plant.

3. Transportation of restmll through port to Incineration plant.


These three sub model are compared with each other and are evaluated for their performances.


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8

Results and Analysis

The proposed models are solved with help of using modelling programming language LINGO

as mathematical software. All the data is needed by the model prepared as data base in externalfiles. Four scenarios are generated to take into account the different combination of the issues.

The criteria for choosing the optimal combination are the minimum value of the objective func-

tion, including the costs and CO2 emission.

In the first scenario, the actual condition of waste transportation in the city is described. Here

the vehicles leave from the garage to source then source to direct waste handling plant without

using transfer station.

Scenario 2:

The transfer station concept is introduced to improve the MSW collection system for each type

of waste. Moreover the model has a possibility to send the waste directly to waste treatment

plant or waste is first send through transfer station, where it is compacted and transported to

waste handling treatment plant in big vehicles.

The table below gives the waste allocation for the scenario 2 (refer Appendix D for detailed

results). From the table a clear distribution of waste is observed. For the case paper waste it is

observed that utilisation of TS1 and TS4 is done and remaining two transfer station are not util-

ised and similarly for waste type compost its evident that the TS2 is unused. It can be concluded

that while redrawing strategy for waste distribution two TS2 and TS3 for paper and TS2 for

compost can be eliminated. But for waste type gelbe if observed, there is under utilisation of

transfer stations. More than 70% of waste is directly transported and remaining through TS3. Insuch case it would be better not to have TS for gelbe. In the case restmll, the distribution is

quite even but still some of the waste directly transported to Incineration plant this is due prox-

imity of the waste source point to the plan.

Table 5: Waste allocation for Scenario 2

(Ton) PAPER KOMPOST GELBE RESTMLL

TS1 113.4702671 202.6053466 0 690.0506345

TS2 0 0 0 770.1969625

TS3 0 425.7138654 126.1979617 1055.635315

TS4 363.6312241 576.6391746 0 1866.408115

WH 362.3715087 293.9516133 307.5208383 722.8249734


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Scenario 3:

Third scenario, in this model the concept of intermodality in waste transportation is introduced.

It is observed from the present geographical situation that the rest-mull can be sent to the incin-

eration plant by ship due presence of ports on either side of river Rhine. So strategically twoports in north and south are selected which will serve the purpose of collecting waste and trans-

porting it by ship to the incineration waste. All other three wastes are sent by transfer station as

stated in above model.

Figure 20: Location of the ports

The table below gives the waste allocation for the scenario 3 (refer Appendix D for detailed

results). It is observed the allocation for waste type paper and gelbe remains as same for the

scenario 2 but for waste type shows equal distribution and utilisation of all the transfer stations.

In waste type restmll, here transfer stations are replaced by the ports. Port2 transports maxi-

mum of the waste amounting to 60% and followed by port 2.


(Ton) PAPER COMPOST GELBE RESTMLL

TS1 113.4702671 202.6053466 0 H1 1146.739934

TS2 0 293.8679043 0 H2 2904.650912

TS3 0 330 126.1979617

TS4 363.6312241 330 0

WH 362.3715087 342.4367491 307.5208383 WH 1053.725154

Scenario 4:In the fourth scenario, model is modified version. Here combined effect of transfer station and

port together for the restmll are analysed. A decision for the waste to be sent through transfer

station or via port or directly to incineration plant is to be made. While for other three waste all

other conditions apply same.


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The observation for the for the waste type paper, kompost and gelbe remain same. Now it is

quite interesting to see flow of waste for restmll where port 2 and TS4 turn out to be major

carrier for the waste flow. An equal distribution is seen for port 1 and TS3. Even the overall

transportation distance is reduced but utilisation of all facilities increase the cost which due to

operational and facility cost.


(Ton) PAPER KOMPOST GELBE RESTMLL

TS1 113.4702671 202.6053466 0 HF1 792.1135449

TS2 0 293.8679043 0 HF2 1671.471173

TS3 0 330 126.1979617 TS3 773.8162146

TS4 363.6312241 330 0 TS4 1233.179739

WH 362.3715087 342.4367491 307.5208383 WH 634.5353281

Overall Comparison of Scenarios:

The graph shows the comparison between all the four scenarios with respect to cost and disance.

With scenario2, 3 and 4 the total distance travelled is reduced but when compared to the cost,

scenario 2 and 3 show low values. Its feasible to apply scenario 2 and 3. But when a

combination of scenario 2 and 3 is tried which is shown by scenario 4 there is rise in cost.

Figure 21: Total cost and Distance for Scenarios

Thus inidividual application of scenario2 and 3 is benifical. This result shows total effectiveness

of transportation by water. Scenario 4 which shows rise in cost this due to the almost even dis-

tribution of waste through all the port and transfer stations. Thus adding to the operational costs

and the fix cost for the ports and transfer stations.


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CO2 emission

This is a comparison for the CO2 for all scenarios. The emission has been reduced compared to

scenario 1. This is due to the reduced travelling distance. Thus it can be said that change in lo-

gistic activity by introduction of transfer station or intermodal transport can help to reduce topollutions. A proper logistic strategy can also help in cutting down emissions.

Figure 22: CO2 emission for various scenarios

From the above analysis, following conclusions can be made:

1. For waste type paper waste, TS2 and TS3 can be eliminated, and thus restricting it for

only two transfer stations.

2. For waste type compost show utilisation of all transfer stations.

3. For waste type gelbe, with respect to quantity it would be rather better to send the waste

directly to the waste handling plant using transfer station

4. For waste type restmll, it is observed scenario 2 and 3 turn out to give good results,

while scenario 4 reduces transportation distance but adds to increased cost when com-

pared to scenario 2 and 3.


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Scenario 5:

The below is the map of Duisburg with the waste location points and its geography

Figure 23: Waste source points in Duisburg

Total amount of waste collected per two weeks: 5105,116 ton

Capacity of small truck: 10 ton (8 ton filling capacity)

Capacity of large truck: 30 ton

Capacity of barge or ship: 1500 ton

Table 8: Total distance travelled (km) (including empty trip+full load)

Km Direct Oberhausen 2 TS 2 hafenTotal distance 14827,1 10239,2 9546,616

Distance with fullload

8982,4 4906,7 4079

Distance withempty load

5844,7 5332,5 5408


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Figure 24: Total distance travelled (km) (including empty trip+full load)

Table 9: Cost for Scenarios

Direct Oberhausen 2 TS 2 PortTotal cost 2325434 1869607 1790679

Total Transportationcost

1559666 961448.3 839679.5

Transportation costfor full load

1442772 854798,3 731455,2

Transportation costfor empty trip

116894 106650 108224,3

Transshipment cost(handling cost)

114389,5 145232,5

Fixed cost for TS orPort

28000 40000

Incineration cost 765767,4 765767,4 765767,4

It is evident from the above result tables that with introduction of transfer station or waterway

transportation there is a significant chance in the total cost and distance. It s observed that the

total distance travelled and total cost for transfer station and port is moreover less same with

little difference.

Load carried per km =

Table 10: Waste transported per km

Direct Oberhausen 2 TS 2 port

Ton / km 0.56 1.02 1.25

Success of a transfer station or port is shown by waste carried per run. This can be done by us-

ing large capacity vehicles. But in small streets such is not possible. So a larger transportation

vehicle from TS to facility or ship for transportation can be used.


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Empty trip travelling =

Table 11: Empty distance travelled

Direct Oberhausen 2 TS 2 port0.394 0.52 0.56

Empty trip is one of the major concern regarding transportation costs. With TS and port the

empty trip distance increase due to empty distance been travelled from garage to source, source

and TS or port and from port to garage.

Here the comparison is made between the unit cost required to transport per ton.km and the

distance travelled from each source point. The data selection for this following graph has been

done as follows:

The following graph is plotted against /ton.km against total km

/ton.km is calculated as follows:Each source is considered separate and its total distance that it makes and cost during his trip arecalculated.

A example in the table for the calculation.

Table 12: Sample calculations

Source Q(ton) d(km

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