CogInfoCom 2013 • 4th IEEE International Conference on Cognitive Infocommunications • December 2–5, 2013 , Budapest, Hungary
A reverse logistics network design model for sustainable treatment of multi-sourced Waste of Electrical and Electronic Equipment (WEEE) Hao Yu
Wei Deng Solvang
Department of Industrial Engineering Narvik University College Narvik, Norway E-mail:
[email protected]
Department of Industrial Engineering Narvik University College Narvik, Norway E-mail:
[email protected]
Abstract—Sustainable development in terms of both economy and environment has become as one of the most critical driving forces in shaping and reforming the management system of waste of electrical and electronic equipment (WEEE). The achievement of the balance between economic and environmental performances requires comprehensive analysis and appropriate design of reverse logistics network of WEEE management system. This paper presents a network design model for treating multi-sourced WEEE in a sustainable manner. Economic efficiency and environmental influence in terms of greenhouse gas emission associated with the transportation of WEEE are defined as two main criteria to determine the network configuration. A conceptual model and a mathematical model are designed to illustrate the systematic formation of the reverse logistics network of WEEE, and the computational programming is then developed in Lingo 11.0. Finally, a hypothetical example is presented in order to enable the understanding of the model in practice.
in 2003. In addition, the large numbers of WEEE imported from developed countries are treated in low-tech ways in order to recapture the value of recourse and materials, which may result in serious environmental problems. Although the transboundary movement of hazardous waste has been contained and forbidden by Basel Convention as well as other national, regional and local legislation, the intercontinental transportation of WEEE from the developed countries in North America and Europe to developing countries in Asia and Africa is still at a considerable amount. Li et al. [4] specifies the input and output countries of WEEE transboundary movement, which is illustrated in Table I.
Keywords—reverse logistics; network design model; sustainable treatment; WEEE
Canada
I.
TABLE I.
Continent America
INTRODUCTION
Europe
With the rapidly technological development and largely shortened life cycle of electrical and electronic equipment (EEE), quantities of the waste of EEE (WEEE) around the world have been increased significantly. An annual amount around 14-20 million personal computers scrapped in United States is estimated by Dat at el. [1], and they also point out the annual WEEE generation in Western Europe is 6 million tons in 1998. The study initiated by European Commission (EC) [2] has revealed that the number of WEEE generation across EU27 has increased to between 8.3 and 9.1 million tons in 2005. The expected annual increase rate of WEEE generation in European countries is between 2.5% and 2.7% in coming years, and the annual generation will increase to about 12.3 million tons by 2020 [2]. In some developing countries, the focus on booming economy makes the treatment of WEEE become a dilemma. Generally, the prosperity of EEE market has a great contribution to national economy, while on the other hand, it leads to a significant increasing of WEEE. Liu et al. [3] estimated, in mainland China alone, around 1.6 million scrapped electrical and electronic equipments were generated
978-1-4799-1546-0/13/$31.00 ©2013 IEEE
TRANSBOUNDARY MOVEMENT OF WEEE (ADAPTED FROM [4])
Asia
Transboundary movement of WEEE Output country
Input country
United States EU-27 Norway Japan
Cambodia
Singapore
China
South Korea
Vietnam India Malaysia Thailand Benin Ghana
Africa
Kenya South Africa
Oceania
Australia
To avoid transboundary movement of WEEE needs not only stringent institutional and legislative mechanism, but also great efforts on developing proper regional and local management system which balances the economy and environmental sustainability. The appropriate network design
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H. Yu and W. D. Solvang • A reverse logistics network design model for sustainable treatment of multi-sourced Waste of Electrical…
appliances.
and development of WEEE management syystem are therefore of fundamental importance. Many studies annd researches have already done a lot to discuss the optimal netw work configuration of WEEE management.
The previous studies and researches of reverse logistics network of WEEE mainly focuus on the minimization of costs through optimal network coonfiguration. The most usual categories of costs are relatedd to, i.e., waste transportation, processing, recycling and dispposal. Some studies have also taken the fulfillment of EU and national legislations into consideration. However, the environmental e impact related to the treatment of WEEE is rarely referred in these studies. The research in this paper aims to reemedy this deficiency.
An early study conducted by Chang et al. [5] proposed a three-stage mixed integer programming (MIP) model to minimize the overall costs through optimal planning p of reverse logistics infrastructures of WEEE. Grunow w and Gobbi [6] developed a decision support tool which toook into account of the implementation of EU Directive off WEEE for the assignment of producers and municipaliities to different collective scheme of waste, and this model has been tested in the reverse logistics network of WEEE inn Denmark, where great improvements were achieved throughh reassignment of each relevant actor. Liu et al. [7] posed a netw work design model for planning the WEEE management system in Guangxi province, China, and a simulation of WE EEE management system conducted by Flexsim software is alsoo presented.
This paper develops a noveel network design model which takes into consideration of both b economic efficiency and environmental impact in netw work planning for sustainable management of WEEE. In this paper, the indicator for evaluating environmental impaact is greenhouse gas emission associated with the transportattion of WEEE, which has been proved to be the culprit acccelerating global warming and climate change. The conceptuaal network model, mathematical model and computational proggramming are formulated in the subsequent sections to system matically illustrate the reverse logistics network of WEEE, annd a hypothetical example is also given at the end for gaining a better b insight into the application of this new model.
To take into account the uncertain influuencing factors of reverse logistics network of WEEE, Liu et al. [8] upgraded a previous model using triangular fuzzy number to replace some exact parameters so as to deal with the uncerttainty. A two-stage optimization model for reverse logistics netw work of WEEE is proposed by Zhi et al. [9], the purpose off this model is to minimize the operating costs and transportaation costs through selecting, locating and allocating waste to collection center, recycling center as well as disassembly cennter, and a genetic algorithm is also designed accordingly for model m computation. Gomes et al. [10] delivered a genetic mixxed integer linear programming (MILP) solution to develop thee recovery network of WEEE in Portugal in order to fulfill the shhort term and long term objective set by EU Directive of WEE EE and transposed Portuguese legislation. The simultaneous miinimization of five relevant costs: waste processing costs, disposal costs, transportation costs, storage costs and com mpensation fee for each sorting center (operational costs), is thhe primary goal of this model.
II.
CE LOGISTICS NETWORK WEEE REVERC
The concept of reverse logiistics was first introduced in the late 1980s, and it is always referred as the collection and transportation of used productss and packages [11]. During the past two decades, reverse logistics l has been intensively referred, discussed, studied andd developed in a great number of researches [11-16]. In the treatm ment system of WEEE, the goal of reverse logistics as depicted by Machado et al. [17] is to plan and operate waste managem ment system efficiently and effectively through minimizzation of WEEE generation, maximization of reuse and reecycling of WEEE, and proper disposal. In this section, a multi-echelon m reverse logistics network which consists of the collection, pre-treatment, treatment, market and dispossal of multi-sourced WEEE is proposed in Fig. 1.
Dat et al. [1] formulated a multi-echeelon mathematical model to optimize the reverse logistics costs for reuse, remanufacturing and recycling of end-of-usee (EOU) and endof-life (EOL) EEE. This study takes into consideration and emphasizes the difference between transpoortation costs and processing costs of different parts of electriical and electronic
As shown in the figure, thhe first step of reverse logistics network starts from the coollection of WEEE. Scrapped electrical and electronic produucts are either delivered to the retailers (i.e., supermarket, stoore of electrical and electronic
Fig. 1. Conceptual model of reverse logistics network of WEEE.
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CogInfoCom 2013 • 4th IEEE International Conference on Cognitive Infocommunications • December 2–5, 2013 , Budapest, Hungary
appliances, etc.) or public drop-off sites by consumers. Certain amount of WEEE comes from the collecting service performed by third party service providers. The WEEE from collecting points will then be transported to and treated at pre-processing facilities where they can be disassembled into parts and components and classified into different categories. Different parts and components of WEEE will subsequently be transported to and treated at the respective facilities for reuse, repair, remanufacturing and recycling in order to recapture value. The parts and components that are not reusable will be sent to incineration or landfill. In this process, parts and components that possess hazardous material, such as capacitors, batteries, printed circuit boards, liquid crystal displays, toner cartridges, etc. [18], needs to be treated with caution. For instance, special designed containers for storage of scrapped batteries in order to avoid leakage of hazardous substances. The leakage of one button battery can contaminate 600000 liter water which equals to the life demand of a person [8]. Some parts and components will be able to re-enter the market and second-hand market after they have been refurbished or remanufactured, and the residues from the treatment will be disposed at incineration plant and landfill as well.
•
B. Notations
As shown in Fig. 1, the value of WEEE increases from upstream operations (collection and pre-treatment) towards downstream operations (treatment and market) excluding the disposal. This has implied the more the reused or recycled products re-enter the market, the more value will be recaptured from the treatment of WEEE. In addition, as an excess transportation of WEEE will not only result in a significant increasing on the costs, but also negative influence on the environment in terms of, e.g., increased amount of greenhouse gas emission, it needs to be included in consideration of overall management system design for WEEE. III.
MODEL FORMULATION
A. Assumptions To formulate the reverse logistics network and resolve the mathematical model, various assumptions are first made. •
The numbers and locations of collection points of WEEE are fixed.
•
The numbers and location of secondary market for reused products of WEEE are known or can be roughly estimated.
•
Information on the amount of the unit transportation and processing costs of WEEE, emission factor of transport vehicles, distance between facilities, candidate locations and planned capacities for potential facilities as well as other relevant parameters are accessible.
•
The variable processing costs of WEEE are linearly related and directly proportional to the waste amount.
•
The costs and greenhouse gas emission from the transportation of WEEE are linearly related and directly proportional to the waste amount and the transported distance.
Uncertainty is not taken into account.
A, a
The set and index of collecting points;
B, b
The set and index of candidate locations of pre-treatment facilities of WEEE;
C, c
The set and index of candidate locations of treatment facilities of WEEE;
D, d
The set and index of candidate locations of disposal facilities of WEEE;
E, e
The set and index of secondary market;
Fb, Fc, Fd
The fixed facility costs of b, c and d;
fb, fc, fd
The flexible treatment costs of b, c and d;
vb, vc, vd
The amount of WEEE treated at facility b, c and d;
vab, vbc, vbd, vcd, vce
The amount of WEEE transported between a and b, b and c, b and d, c and d, c and e;
Tab, Tbc, Tbd, Tcd, Tce
The unit transportation costs of WEEE between a and b, b and c, b and d, c and d, c and e;
Pce
The Unit price of reused product from facility c in secondary market e;
Eab, Ebc, Ebd, Ecd, Ece
The greenhouse gas emission factor between a and b, b and c, b and d, c and d, c and e;
dab, dbc, dbd, dcd, dce
The distance between a and b, b and c, b and d, c and d, c and e;
Inec
Conversion factor of emission-currency;
WAa
The amount of WEEE at collecting point a;
tc
The conversion rate of Waste-to-new product;
Cb, Cc, Cd
The capacity of facility b, c and d;
DSb, DSc, DSd
Binary decision variable for candidate point b, c and d, if it equals to 1, the candidate point is selected for building new facility, if it equals to 0, otherwise.
C. Model In this section, two objective functions as well as ten sets of constraints are formulated to determine the reverse logistics network configuration of WEEE management simultaneously
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H. Yu and W. D. Solvang • A reverse logistics network design model for sustainable treatment of multi-sourced Waste of Electrical…
respectively. Eq. (8) guarantees the WEEE can be treated at facility b only when the candidate location b is selected for constructing the treatment facility and the amount of WEEE treated is less than the capacity. Eq. (9) and Eq. (10) are capacity constraints for facility c and d, respectively. Eq. (11) determines whether the candidate locations of facility b, c and d are chosen for constructing new facilities. Eq. (12) restricts the amount of WEEE transported between or treated at different facilities is non-negative value.
taking into consideration of both economic and environmental aspects. Minimize:
Herein, it is note while that the achievable minimum costs could be equal to a minus value if the profit generated from the resale of new products originated from WEEE is more than the costs for operating the treatment system, which means the whole reverse network of WEEE is profitable. The purpose becomes therefore to obtain the maximum profit of WEEE treatment, and this can be easily achieved through assigning the minus sigh to each side of Eq. (1). In this model, the reason why profit-maximization objective is replaced by costminimization objective is to maintain the consistency with the minimization of greenhouse gas emission objective, which greatly decrease the level of difficulty in equation merger and model computation. Although the profit-maximization is not defined as an independent objective function, it is obviously from the discussion above that the less the minus value of Eq. (1) reaches, the more profits will be generated from the treatment of WEEE. In addition, the conversion factor Inec introduced in Eq. (2) is to monetize the greenhouse gas emission from the transportation of WEEE, and the unit of both Eq. (1) and Eq. (2) can be unified by such mean. The two objective functions can therefore be easily combined through allocating respective weight to each of them, as shown in Eq. (13). The weight of each objective function represents the relative importance of them in the decision-making process.
1
2 Subject to: ,
3 ,
4
,
5
,
Minimize: 13
6 IV.
,
In this section, a hypothetical example is given for presenting a better inside into the model application. It is assumed that there are 10 collecting points in the studied area, and the average annual generations of WEEE at those collecting points are 97 ton, 59 ton, 134 ton, 219 ton, 84 ton, 58 ton, 230 ton, 76 ton, 125 ton and 242 ton, respectively. In the studied area, 3 candidate locations for disassembling center and 3 candidate locations for recycling plant have already been selected so as to recapture value from WEEE. There is no disposal facilities existed in this municipality, and the residue from the disassembling center and recycling plant will be transported to and treated at the incineration plant located in neighbor municipality. Therefore, the costs for operating the disposal facility is unnecessary to be taken into account, but a gate fee of 900 unit currency/ton must be paid for dealing with the residue from WEEE treatment to the incineration plant in neighbor municipality. Herein, the type of currency is not specified in the studied area, so the unit of costs is represented by unit currency, and this will be applied in the subsequent part of this section. The unit transportation costs and distance among the collecting points, incineration plant, secondary market, candidate locations for pre-treatment facilities and
7
,
8
,
9 ,
10
0,1 ,
0,1 ,
0,1 ,
,
, 11
0, 0,
0,
0, 0,
0, ,
,
0, ,
0, ,
HYPOTHETICAL EXAMPLE
12
Eq. (1) and Eq. (2) present the main objectives of this network model, one is to minimize the costs for operating the treatment system of WEEE, and the other is to minimize the greenhouse gas emission from the transportation of WEEE. Eq. (3) restricts that the WEEE collected in collecting point a can be totally transported out and managed for further treatment. Eq. (4), Eq. (5), Eq. (6) and Eq. (7) are mass balance requirements for facility b, c and d, and secondary market e,
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CogInfoCom 2013 • 4th IEEE International Conference on Cognitive Infocommunications • December 2–5, 2013 , Budapest, Hungary TABLE V. FIXED FACILITY COSTS, FLEXIBLE TREATMENT COSTS AND CAPACITY OF PRE-TREATMENT FACILITY, TREATMENT FACILITY AND DISPOSAL FACILITY
treatment facilities are given in Table II, Table III and Table IV, respectively. The fixed facility costs, flexible treatment costs and capacity are presented in Table V. TABLE II. UNIT TRANSPORTATION COSTS AND DISTANCE BETWEEN COLLECTING POINTS AND THE CANDIDATE LOCATIONS OF PRE-TREATMENT
Index
FACILITIES
Fixed Facility Costs (Unit currency), Flexible Treatment Costs (Unit currency) and Capacity (Ton/year) of b, c and d F f C
Unit Transportation Costs (Unit currency) and Distance (km) between a and b b1 b2 b3
b1
550000
250
1500
b2
680000
250
1800
b3
600000
270
1500
T
d
T
T
d
T
800000
290
1200
a1
26
3.2
30
4
45
5.5
c1
750000
320
1100
a2
45
5
28
3
90
8.9
c2
275
1100
a3
23
3
86
10.1
37
5
c3
720000
d
0
900
6000
a4
28
3.2
19
4
28
5
a5
58
6.5
39
3.8
40
4
a6
58
5.5
37
2.9
15
1.2
a7
26
3
47
5
50
5
a8
39
4
26
3.1
30
2.5
a9
45
5
52
5
71
8
a10
31
4
42
5.2
32
4
Index
TABLE VI. SELECTION OF DISASSEMBLING CENTER AND RECYCLING PLANT, ANNUAL AMOUNT OF WEEE PROCESSED AT EACH FACILITY, ANNUAL AMOUNT OF WEEE TRANSPORTED AMONG FACILITIES AND ANNUAL AMOUNT OF RECYCLED PRODUCTS ENTERED THE SECONDARY MARKET
Index
TABLE III. UNIT TRANSPORTATION COSTS AND DISTANCE BETWEEN THE CANDIDATE LOCATION OF PRE-TREATMENT FACILITIES AND THE CANDIDATE LOCATION OF TREATMENT FACILITIES AND DISPOSAL FACILITIES
Index
Unit Transportation Costs (Unit currency) and Distance (km) between b and c, d b1 b2 b3 T
d
T
T
d
Selection of disassembling center and recycling plant, annual amount of WEEE processed at each facility (ton), annual amount of WEEE transported among facilities (ton) and annual amount of recycled products entered the secondary market (ton) vTransport DS vProcessed c3 d e
b1
--
--
--
--
--
b2
--
--
--
--
--
T
b3
Selected
1324
1100
224
--
--
--
--
--
--
c1
34
4
15
2
31
4.2
c1
c2
28
3
21
3.1
36
4.5
c2
--
--
--
--
--
c3
42
4.5
24
3
19
1.5
c3
Selected
1100
--
495
605
8
d
Existed
719
--
--
--
d
85
9
90
11
75
TABLE IV. UNIT TRANSPORTATION COSTS AND DISTANCE BETWEEN THE CANDIDATE LOCATION OF TREATMENT FACILITIES AND THE CANDIDATE LOCATION OF DISPOSAL FACILITIES AND SECONDARY MARKET
Index
After all relevant parameters have been assumed, the model was resolved by using professional optimization software Lingo 11.0. The Lingo programming for model computation was run in a personal laptop with Intel Core2 2.53 Hz processor, 2.72 GB RAM and 100 GB hard drive capacity under Window XP Professional (2002), and 6 seconds were used for performing 4930 iterations to calculate the optimal network configurations of WEEE treatment system. Table VI presents the result of the optimal configuration of reverse logistics network in given area. As shown in the table, candidate location b3 and c3 are selected, respectively, to build the dissembling center and recycling plant. All the WEEE collected in the studied area will be first treated at b3 and then transported to c3 for recycling and d for disposal, and c3 works on full capacity to supply as many as possible of the recycled products to the secondary market e for recapturing value from WEEE.
Unit Transportation Costs (Unit currency) and Distance (km) between c and d, e c1 c2 c3 T
d
T
T
d
T
d
32
3.8
45
4.7
23
3
e
42
5.1
45
4.3
58
6.7
For disassembling center and recycling plant, there is no limitation for the maximum number to be constructed. The conversion rate of waste-to-new product at recycling plant c1, c2 and c3 are 56%, 58% and 55%, respectively. The unit price of recycled products from recycling plant c1, c2 and c3 in secondary market e are 950 unit currency/ton, 1020 unit currency/ton and 995 unit currency/ton, respectively. In this hypothetical example, the emission factors are set to be equal to 5 in all routes, and the conversion factor of emissioncurrency is 1 unit currency/ton km. To composite the costminimization objective function and greenhouse gas emissionminimization objective function, the weight of them are assigned to be 0.6 and 0.4, respectively.
Under the optimal network configuration, the optimal overall costs including the converted costs related to greenhouse gas emission are 1329.8 103 unit currency. Although the value can be significantly recovered through the reuse, repair, remanufacturing and recycling of WEEE, it still needs a great investment to operate the whole management
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H. Yu and W. D. Solvang • A reverse logistics network design model for sustainable treatment of multi-sourced Waste of Electrical…
system of WEEE, and if we include the costs for facility construction and maintenance, the incremental costs could become a burden for the one who manage and operate the reverse logistics network of WEEE. This has revealed the current situation why government has to provide companies with subsides and incentives in order to encourage them to have the enthusiasm and participate in the reverse value chain of WEEE recovery. V.
[3]
[4]
[5]
CONCLUSION
In this paper, we develop a two-objective MILP model simultaneously taking into account of both economic and environmental issues for determining the configuration of reverse logistics network of WEEE. Different from the single cost-minimization models proposed in previous studies, the environmental problems caused by greenhouse gas emission from the transportation of WEEE is emphasized and formulated in this new model in order to force the result moving towards more environmentally friendly and sustainable management of WEEE. The conceptual model of reverse logistics network and reverse value chain of WEEE is first established, and the relationship of five main actors within the reverse value chain, including collection of WEEE, pretreatment, treatment, disposal and resale in the secondary market, is thoroughly presented in this conceptual model. The MILP model is then formulated accordingly for mathematically illustrating the reverse logistics network of WEEE and performing the optimization of network configuration. To composite the two objective functions in this model, the conversion factor is introduced to monetize the greenhouse gas emission. The program for model computation is developed in professional optimization software Lingo 11.0, and a hypothetical example is also given to obtain a better insight into the model application. The result of the hypothetical example is briefly discussed as well to give a vivid picture of the current situation of the management of WEEE.
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
Although this study has investigated greenhouse gas emission in the design and optimization of the reveres logistics network of WEEE, there are still some other environmental indicators need to be focused on, for instance, the treatment for some parts and components from WEEE may lead to secondary pollution, and much more costs and energy will be consumed to recover the environment than the profit generated by such means. In addition, the uncertain parameters are not taken into consideration in this model, however, some parameters may exit uncertainties, i.e., the generation of WEEE and market needs of recycled products. Therefore, the further study addressing more comprehensive environmental issues and uncertain parameters are suggested in future.
[15]
[16]
[17]
[18]
[19]
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