Transportation Research Part D 14 (2009) 291–299

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Transportation Research Part D

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Vehicle routing in reverse logistics for recycling end-of-life consumerelectronic goods in South Korea

Hyunsoo Kim a, Jaehwan Yang b,*, Kang-Dae Lee c

a Department of Industrial Engineering, Kyonggi University, Youngtong-gu, Suwon 443-760, Republic of Koreab School of Business, University of Seoul, Dongdaemun-gu, Seoul 130-743, Republic of Koreac Korean Standards Association, Gangnam-gu, Seoul 135-513, Republic of Korea

a r t i c l e i n f o

Keywords:Vehicle routing problemTabu searchReverse logisticsRecycling

1361-9209/$ - see front matter � 2009 Elsevier Ltddoi:10.1016/j.trd.2009.03.001

* Corresponding author. Tel.: +82 19 9164 8397;E-mail address: jyang@uos.ac.kr (J. Yang).

a b s t r a c t

This paper presents a vehicle routing approach for the transport of end-of-life consumerelectronic goods for recycling in South Korea. The objective is to minimize the distanceof transportation of end-of-life goods collected by local authorities and major manufactur-ers’ distribution centers to four regional recycling centers located. A vehicle routing prob-lem is constructed for each regional center, and a Tabu search is applied to solve it.Computational results using field data show that the method outperforms existingapproaches to reverse logistics.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

The concept of extended producer responsibility (EPR) assigns the responsibility of recycling end-of-life (EOL) goods andpackaging to the manufacturers who collect their disposed EOL goods from the field and from consumers, and also ensure theenvironmental ‘friendliness’ of through their recycling up to final disposal. In 2003, Korea initiated laws making it mandatoryfor the manufacturers of consumer electronics to take responsibility of recycling their EOL goods. The manufacturers pay finewhen they cannot fulfill their recycling quotas.

We use the vehicle routing problem (VRP) to develop logistics for EOL handling of South Korea’s consumer electronicgoods by solving the reverse logistics problem of each designated regional recycling center. This is done by minimizingthe distance for moving EOL goods collected by local authorities and major manufacturers to the assigned RCs.

2. Background

Studies on supply chains have generally focused on topics encompassing production to the point of sale through distri-bution. However, reverse logistics has recently become an area of active study. According to the Council of Supply ChainManagement Professional (2008), reverse logistics is the process of planning, implementing, and controlling the efficientflow of goods and related information from the point of consumption to the point of final disposition. Therefore, reverselogistics is a fundamentally required process, whereby disposed consumer electronic goods are collected and delivered toa RC location for recycling and/or final disposition (Stock, 1992).

We utilize the VRP to address the reverse logistics problem. In the VRP, the vehicle can either deliver or pick up goodsfrom customers, and the objective is to determine delivery or collection routes for each vehicle with minimal transportationcosts. The VRP has been used extensively to solve logistics problems, for instance by the Hong Kong Post (Ji and Chen, 2007).

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292 H. Kim et al. / Transportation Research Part D 14 (2009) 291–299

The problem is to schedule a set of n collection points (district post offices) from a depot (general post office) on Hong KongIsland. Its objective was to maximize resource utilization and minimize operation costs. Schultmann et al. (2006) also usedthe VRP to solve the end-of-life-vehicle (ELV) components’ backhaul problem in Germany by modeling the ELV components’backhaul from the EOL collectors to reprocess facilities as a symmetric capacitated VRP with one reprocessing site and amaximum tour distance. The primary objective was to generate a tour schedule at minimal cost.

Because the traveling salesman problem (TSP) is nondeterministic polynomial time (NP) hard (Miller and Thatcher, 1972)and the TSP is a special case of the VRP with only one tour, VRP is also NP-hard. Because the VRP is NP-hard, it is impossibleto find polynomial time algorithms, and thus many researchers have focused on the development of efficient heuristics. Ithas been shown that heuristics using Tabu searches generate good results (Laporte et al., 2000).

3. Reverse logistics for recycling EOL consumer electronic goods in South Korea

The Korean Association of Electronics Environment (2006) argues that the useful life of consumer electronic goods isslightly shortened every year, corresponding to the change in consumers’ propensity to consume. Table 1 shows the annualquantities of disposed EOL consumer electronic goods between 1999 and 2006.

Since 2003 the government has continuously increased the amount of recycling that manufacturers and related vendorsof EOL consumer electronics goods must process (Table 2).

There are currently four major automated regional RCs as recycling facilities for EOL consumer electronic goods in SouthKorea: MRC for the metropolitan region, ARC for the midwest (Chung Bu) region, CRC for the mideast (Yong Nam) region, andHRC for the southwest (Ho Nam) region. Each regional RC covers a certain regional area, and thus EOL goods disposed from acertain region (i.e., metropolitan area) should be delivered to the specific RC (i.e., MRC) responsible for that region. Fig. 1illustrates the locations of four major regional RCs in South Korea.

According to the Korean Association of Electronics Environment (2007), there are two channels of reverse logistics for therecycling of disposed EOL consumer electronic goods. Each channel is utilized to pick up or collect EOL goods disposed by theconsumers, and to deliver them to one of the four regional RCs. The first channel is employed when consumers purchase newelectronic goods. Through this channel, a retailer delivers a new product, for example a refrigerator, to a consumer’s location.When the new refrigerator is delivered to the location, the retailer is obligated to pick up the old refrigerator if desired by thecustomer. Currently, the retailers pick up the old electronic goods from the consumers’ location free of charge for the purposeof customer service. Then, the retailers send the collected EOL goods to manufacturers’ distribution centers. Whenever a cer-tain quantity of EOL goods is collected at a manufacturer’s distribution center, EOL goods are delivered to the appropriateregional RC for the recycling process.

The second channel is employed when a consumer wishes to dispose her/his used consumer electronic goods. In this case,the consumer must pay a fee for disposal to a designated local authority. Similarly, whenever a certain quantity of EOL goodsis collected at a local authority’s collection center, EOL goods are delivered to its regional RC for recycling. Fig. 2 illustratesthe current two channels of reverse logistics for the recycling of EOL consumer electronic goods in South Korea.

4. VRP for EOL consumer electronic goods in South Korea

4.1. New VRP model

The South Korean reverse logistics system for the recycling of EOL consumer electronic goods is inefficient in terms ofdelivering the collected EOL goods by collection centers, such as local authority’s collection centers and manufacturers’DCs, to their regional RC. Currently, each of 126 local authorities’ collection centers and 110 manufacturers’ DCs has itsown vehicles or has a contract with third-party logistics companies, and it makes individual decisions regarding the deliveryof collected EOL goods to its regional RC. The delivery decision is generally triggered by the inventory level of EOL goods ineach collection center, but there is no optimal procedure to determine the timing and quantity of the delivery. Hence, it ispossible that the capacity utilization of each vehicle is low and consequently, the distance traveled may not be minimized.Moreover, the quantity of EOL goods received by a regional RC varies depending on the number of trucks they receive each

Table 1The amount of disposed EOL consumer electronic goods in South Korea (thousand units).

Year Refrigerator Washer Air conditioner TV

1999 1804 1493 141 6952000 1765 1350 180 7202001 1747 1534 274 6982002 1743 1524 311 9602003 1861 1545 297 9682004 1840 1531 288 10612006 1869 1358 402 1245

Source: Korean Association of Electronics Environment (2006).

Fig. 1. Four regional RC’s in South Korea.

Free

Charge C

onsumer Manufacturer’s Distribution

Center

Local Authority’s Collection

Center Regional

RC

Fig. 2. Current reverse logistics for recycling EOL consumer electronic goods in South Korea (simplified).

Table 2Obligatory amount and ratio of recycling for manufacturers and related vendors.

2003 (ton) 2004 (ton) 2005 (ton) 2006 (%) 2007 (%) 2008 (%)

Refrigerator 19,100 26,155 16.5 16.9 17.3 18.9Washer 13,700 15,362 22.8 23.4 24.2 25.3Air conditioner 600 687 1.5 1.7 1.9 2.1TV 8481 9728 11.8 12.6 13.3 14.5

Source: Korean Association of Electronics Environment (2007).

H. Kim et al. / Transportation Research Part D 14 (2009) 291–299 293

day – this may result in overtime and idle time at the regional RC. Therefore, by improving the efficiency of the delivery pro-cess, transportation costs associated with the delivery of EOL goods can be reduced. Further, we can stabilize the daily load ofeach regional RC by spreading its inbound shipments over different dates.

We develop a new reverse logistics mechanism targeted toward minimizing the transportation distance of vehicles dur-ing a fixed period of time. Each regional RC uses its own vehicles to collect EOL goods from local authorities’ collection cen-ters and manufacturers’ DCs within its own region. Each regional RC is assumed to be capable of assessing the inventory levelof EOL goods at each of the collection centers, via the use of some computer information system. Then regional RCs can makedecisions regarding routes and the frequency of collection runs based on truck availability and inventory levels at the targetcollection centers. We also assume that each regional RC has a fixed but sufficient number of identical trucks, and there ex-ists a maximum distance for each truck per day.

With the above assumptions, the problem becomes a symmetric capacitated vehicle routing problem with one depot anda defined maximum tour distance. The VRP is defined on a graph G ¼ ðV ; EÞ in which V ¼ f0;1; . . . ;ng is a vertex set. We re-strict our attention to the undirected case, i.e., E ¼ fði; jÞ : i; j 2 V ; i < jg representing an edge set. Vertex 0 is a depot (regionalRC) whereas the remaining vertices are customers (EOL collection centers). Each vertex of V n f0g is associated with a non-negative demand qi and each edge ði; jÞ is associated with a non-negative cost of length cij. The VRP entails the design of mvehicle routes of least total cost, each beginning and ending at the depot with three constraints where each customer is

294 H. Kim et al. / Transportation Research Part D 14 (2009) 291–299

visited exactly once, the total demand of any route does not exceed the vehicle capacity Q , and the length of any route doesnot exceed the preset maximum route length, L.

4.2. Integer programming model

We now formally describe an integer programming (IP) model for the VRP. We begin by defining a decision variable andsome notation. A decision variable is xv

ij ¼ 1 if vehicle v travels on arc ði; jÞ; and 0 otherwise. We also define n and m as thenumber of EOL collectors except for the depot and number of vehicles available, respectively. We define cij as the distancefrom i to j for i, j = 0,1, . . . ,n and di as supply from the EOL collector i for i = 0,1, . . . ,n. Then, the VRP can be formulated into theinteger programming model:

MinimizeXn

i¼0

Xn

j¼0

Xm

v¼1

cijxvijði–jÞ

Subject toXn

j¼1

Xm

v¼1

xvij ¼ 1 for i ¼ 0;1; . . . ;n ði – jÞ ð1Þ

Xn

i¼0

xvij �

Xn

i¼0

xvji ¼ 0 for j ¼ 1;2 . . . ;n; v ¼ 1;2; . . . ;m ði – jÞ ð2Þ

Xn

i¼1

di

Xn

j¼0

xvij 6 Qv for v ¼ 1;2; . . . ;m ð3Þ

Xn

i¼1

Xn

j¼1

dijxvij 6 L for v ¼ 1;2; . . . ;m ði – jÞ ð4Þ

Xn

j¼1

xv0j 6 1 for v ¼ 1;2; . . . ;m ð5Þ

Xn

i¼1

xvi0 6 1 for v ¼ 1;2; . . . ;m ð6Þ

xvij 2 S for i; j ¼ 1;2 . . . ;n; v ¼ 1;2; . . . ;m ð7Þ

where S ¼ fðxvij : ui � uj þ ðnþ 1Þxv

ij 6 n for 1 6 i – j 6 n for some real numbers uig, Qv is the capacity of vehicle v forv ¼ 1;2; . . . ;m, and L is the maximum time allowed for a tour by vehicle v for v ¼ 1;2; . . . ;m .

Restriction (1) ensures that each vertex must be served by exactly one vehicle. Constraint (2) implies that a vehicle mustleave the vertex it has already entered. Restrictions (3) and (4) require that vehicle capacity constraints and the limitation onthe maximum route duration are satisfied. Constraints (5) and (6) guarantee that vehicle availability is not exceeded. Finally,constraint (7) prevents the need for a sub-tour in the final solution. There are more than 50 centers that EOL consumer elec-tronic goods for a regional RC.

4.3. A Tabu search heuristic

We used the TABUROUTE algorithm (Gendreau et al., 1994) to solve our VRP problems. The TABUROUTE is known to beone of the best heuristics for the solution of relatively large-sized (more than 100 customers) VRPs (Laporte et al., 2000). Weassume that every truck has the same capacity and that there is a limitation on the length of a tour.

The main TABUROUTE algorithm uses three sub-procedures – the generalized insertion (GENI), the unstringing and string-ing (US) developed for TSP by Gendreau et al. (1992), and a SEARCH procedure – to improve on a given solution using Tabusearch. One important feature is that the search process examines solutions that may be infeasible with regard to the capacityor maximum route length constraints. Also, at a variety of points during the search process, TABUROUTE reoptimizes the routein which a vertex has just been inserted by using the US procedure. Additionally, it uses a diversification strategy where itpenalizes vertices that have been frequently moved to increase the probability of considering slow-moving vertices.

5. Analysis

5.1. Input data

There are four regional RCs (MRC, ARC, CRC, and HRC) in South Korea. Table 3 shows the number of EOL goods collectioncenters, each of which delivers the materials collected to the designated regional RCs. Each EOL center is assigned to a spe-cific regional RC by a government authority. Hence, it is reasonable to apply a separate VRP model to each regional RC – con-sequently, we solve four VRPs.

Table 3EOL goods collection centers that are assigned to regional RC’s.

Regional RC MRC ARC CRC HRC

The number of local authority’s collection centers 50 18 42 16The number of manufacturer’s distribution centers 20 22 59 9

Source: Korean Association of Electronics Environment (2007).

H. Kim et al. / Transportation Research Part D 14 (2009) 291–299 295

For each regional RC, we calculate the distance between the EOL collection centers and between a collection center andthe regional RC (a depot). We utilize the shortest rectilinear distance between two nodes by using each location’s longitudeand latitude. The following (Moritz, 1980) is used to calculate the rectilinear distance:

Table 4The am

Node

EOL gooEOL goo

Rectilinear distance ¼ ðjlongitude of origin� latitude of destinationj � 110996:8þ jlongitude of origin� longitude of destinationj � 87754:2Þ=1000: ð8Þ

For verification, we utilize a simple regression equation to estimate the road distance from the rectilinear distance using162 pairs of the rectilinear and the road distances from a global positioning system (GPS) navigation system:

Road distance ¼ 6:0145þ 1:002 � rectilinear distance; R2 ¼ 0:97: ð9Þ

Eq. (9) is used to estimate the distances. However, when two locations are close to each other, say less than 10 km, theestimated road distance is presumably overestimated due to the positive value of the intercept of the y-axis, 6.0145 km. Theoverestimation is exacerbated as the rectilinear distance becomes shorter. Additionally, the slope of Eq. (9) is almost iden-tical to 1 and hence, we employ Eq. (8) rather than (8).

For each collection center, the quantity of EOL goods delivered to its regional RC can be calculated by using shipping datafrom December 2004 to December 2006 except for HRC, where the data are available only from January 2006 to December2006. For computational analysis, we calculate the average daily amount of EOL goods supplied from each collection centerby dividing the two-year EOL goods amount by 520 days, except in the case of the HRC, where it is divided by 260 days.

The maximum distance per route is uniformly set to 600 km, which is the same as in the study of Schultmann et al.(2006). We assume that the number of trucks available should be sufficient, and thus the number of routes is not limited.This assumption is also used in the study of Schultmann et al. (2006). Finally, we assume that each RC utilizes 8 ton trucksonly.

5.2. Computational results

In this section, we present the results of the computational analysis. A VRP is solved for each of the four regional RCs:MRC, ARC, CRC, and HRC. The solution method used is TABUROUTE by Gendreau et al. (1994). The TABUROUTE is codedin Visual Basic language and is run on a PC with an Intel Core 2 Quad CPU at 2.4 GHz and 3.25 GB RAM.

The result for MRC is shown in Table 4, and the results for the other RCs are shown in Tables A1–A3 in Appendix. SomeEOL collection centers that are located on islands were not included in this analysis. Each route is described as a series ofnode numbers. Thus, each node number in the series represents either a regional RC (node 00) or an EOL goods collectioncenter. A pictorial representation of the routes for MRC is presented in Fig. 3.

The results in Table 4 show that 24 routes are required to transport EOL consumer electronic goods from the EOL collec-tion centers to the MRC. It can be observed from the third column that the distance of each route varies significantly from12.4 to 479.2 km. Hence, some combination of routes may be handled by a single truck. For example, the distance of routes 1,2, 3, and 4 is only 339.2 km. Thus, even if we include loading and unloading times, these four routes can be handled by justone truck. Therefore, the entire 24 routes may be covered by far less than 24 trucks.

The fourth column in Table 4 shows that the capacity utilization of each route varies from less than 20% to 100%. Note thatthe quantity of EOL goods from each collection center varies depending on its size and location, and the low capacity utili-zation implies a low supply of EOL goods at some collection centers. Values indicating low capacity utilization, such as 19%,suggest that a truck does not have to be dispatched for each route everyday, and may even mean that a truck should gothrough the route only once per week. Thus, we may require even fewer trucks to handle the entire route.

Finally, it can be observed from Table 4 that some routes are identical, and this implies that this route requires more thanone trip per day. This result is due to the fact that node 03 for MRC has a high supply of EOL consumer electronic goods. In

ount of EOL goods shipped from each EOL goods collection center to MRC (8 ton trucks).

1 2 3 4 5 6 7 8 9 10

ds amount (2 years) 46.14 31.1 850.83 26.22 700.39 22.94 486.7 967.17 7.42 372.81ds amount (1 day) 0.089 0.060 1.636 0.050 1.347 0.044 0.936 1.860 0.014 0.717

Fig. 3. VRP routes generated for MRC.

296 H. Kim et al. / Transportation Research Part D 14 (2009) 291–299

this case, MRC may need to purchase a bigger truck to reduce the number of trips and reduce the transportation cost. Theresults for ARC, CRC, and HRC are similar to those for MRC. In particular, HRC requires only nine routes because it has onlyrecently begun its operation, and may require some time to gear up to full capacity.

The field data from December 2004 to December 2005 were analyzed to estimate the transportation cost of the currentdelivery method from EOL collection centers to the corresponding regional RCs. The datasets we employed contained onlythe number of tons and types of electronic goods delivered each day from each EOL collection center to the correspondingregional RC, and they did not specify the types of trucks or the number of trucks that were utilized. Additionally, the recordsdid not include specific cost information. Hence, we made some assumptions to estimate the cost.

From the record of the weights and types of EOL goods delivered each day from each collection center to the correspond-ing regional RC, we deduced the number of pallets required for each delivery. Then, the numbers of pallets were transformed

Table 5VRP routes generated for MRC.

Route number Routes Distance (km) Truck capacity utilization (%)

1 00 60 61 11 00 44.1 192 00 08 00 62.8 863 00 07 00 62.8 944 00 59 58 06 05 57 00 169.5 875 00 56 55 13 45 00 171.6 1006 00 47 48 46 40 37 00 133.0 897 00 43 39 36 00 137.6 728 00 16 42 50 00 183.2 849 00 44 41 04 09 00 443.9 6010 00 10 12 15 01 25 00 479.2 9211 00 19 29 28 30 34 00 183.0 9712 00 17 35 31 32 18 00 120.0 7613 00 33 27 24 21 22 23 00 116.1 9714 00 20 38 00 86.6 6815 00 49 52 54 51 02 00 87.8 9816 00 53 00 39.1 2117 00 26 14 00 28.2 9918 00 03 00 12.4 6419 00 03 00 12.4 10020 00 05 00 140.6 10021 00 08 00 62.8 10022 00 16 00 143.3 10023 00 16 00 143.3 10024 00 57 00 123.5 100

Table 6Comparison of number of deliveries and traveling distance for each regional RC between the current method and the new VRP method.

RC Current method VRP method

Number of deliveries(5 ton truck)

Number of deliveries(8 ton truck)

Number of deliveries(5 ton and 8 ton truck)

Distance(km)

Number of deliveries(8 ton truck)

Distance(km)

MRC 5033 8387 13,420 1,703,610 10,408 1,411,481ARC 3485 7857 11,342 2,389,878 9244 2,049,177CRC 6203 12,915 19,118 3,483,562 15,325 2,588,120HRC 668 1651 2319 383,625 1893 400,626

Table 7Comparison of transportation cost between the current method and the new VRP method.

RC Current method VRP method Cost reduction

5 ton truck 8 ton truck Cost Cost

MRC $1,562,758 $2,262,583 $3,825,342 $2,041,938 $1,783,404 (47%)ARC $2,037,113 $2,984,940 $5,022,050 $2,680,669 $2,341,382 (47%)CRC $3,018,102 $4,421,689 $7,439,791 $3,562,314 $3,877,477 (52%)HRC $331,711 $498,002 $829,713 $528,217 $301,496 (36%)

Total $6,949,684 $10,167,214 $17,116,896 $8,813,138 $8,303,758 (49%)

Note: Data for MRC, ARC, and CRC are collected from December 2004 to December 2006; the data for HRC are collected from January 2006 to December2006.

H. Kim et al. / Transportation Research Part D 14 (2009) 291–299 297

to the type and number of trucks that might be utilized. We assume that 5 and 8 ton trucks were used, and based on theanalyses of current practices, this assumption appears valid. We assume that whenever possible, a small truck was used,and we also attempted to minimize the use of a number of trucks.

After the number of deliveries for each type of truck used was estimated we calculated the distances of the entire deliv-eries from December 2004 to December 2005 by multiplying the two-way distance from each collection center to the cor-responding regional RC. The results are presented in Table 5, along with the results of the VRP method.

To calculate the distance of the VRP method, we assume that a truck is dispatched to a route when it can achieve 100%capacity utilization. In other words, the dispatching time of a truck to each route is optimized based on the known supply ofEOL consumer electronic goods from each collection center.

Even though one cannot directly compare the distances of the two methods because of the different types of trucks used,distance was reduced by between 292,000 and 895,000 km with the VRP method except for the HRC, where the distanceincreased by 17,000 km due to the small number of EOL collection centers that were associated when the data werecollected.

To compare the performance of the two methods more directly, we estimated the transportation cost of each method.Kim et al. (2007) used a fixed cost of $43.4 and a variable cost of $0.79 per km to calculate the transportation cost of a5 ton truck, and a fixed cost of $48.5 and a variable cost of $1.09 were used for the 8 ton truck. The same costs were usedfor our calculations (Table 6). The transportation cost of the four RCs fell by 49% from the current levels, with the greatestreduction at the CRC, which had the largest number of collection centers, and the smallest reduction occurred at the HRC,which had the fewest centers. Even though the distance increased with the new VRP method at HRC, the transportation coststill fell by 24% indicating that many unnecessary deliveries were made to the HRC by EOL collection centers (Table 7).

6. Conclusions

This paper offers an efficient vehicle routing planning of reverse logistics for the recycling of consumer electronic goods inSouth Korea. A VRP model was developed for each of the four RCs and a Tabu search heuristic was applied to solve the re-verse logistics problem. The VRP model calculates the desirable transportation route within each region to minimize the dis-tance vehicles travel for reverse logistics.

Computational results using field data show that the suggested VRP method outperforms the existing method in SouthKorea. The immense reduction in the transportation cost using the suggested VRP method is indicative of the inefficiencyof the current transportation method. It is particularly important to note that the reduction is bigger in cases in which morecollection centers are associated with a specific regional RC. Therefore, when both the EOL consumer electronic goods andthe number of EOL collection centers increase in the future, the benefits from the suggested VRP method should be expectedto become more significant. Another benefit of using the suggested VRP method is that it can contribute to considerablereductions in the distance, which result in the reduction of CO2 emissions.

Acknowledgement

The authors would like to thank the Korean Association of Electronics Environment for its contribution of field data.

298 H. Kim et al. / Transportation Research Part D 14 (2009) 291–299

Appendix

See Tables A1–A3.

Table A1VRP routes generated for ARC.

Route number Routes Distance (km) Capacity utilization (%)

1 00 17 13 14 06 32 19 33 00 154.1 1002 00 03 05 00 190.4 393 00 07 08 00 179.8 984 00 31 11 12 16 29 30 00 237.3 755 00 18 00 247.0 916 00 15 10 09 00 528.6 627 00 01 27 28 21 00 268.5 998 00 35 22 02 26 25 20 00 187.0 559 00 24 00 78.5 5010 00 23 00 78.6 8811 00 36 34 00 46.9 8512 00 04 00 36.0 3613 00 05 00 187.4 10014 00 07 00 176.0 10015 00 10 00 493.4 10016 00 10 00 493.4 10017 00 10 00 493.4 10018 00 20 00 90.4 10019 00 22 00 122.3 10020 00 25 00 109.6 10021 00 26 00 109.8 100

Table A2VRP routes generated for CRC.

Route number Routes Distance (km) Capacity utilization

1 00 51 01 03 04 00 52.5 852 00 02 05 36 61 38 39 80 57 00 150.4 843 00 53 52 48 49 43 42 40 41 00 148.5 1004 00 46 54 55 37 60 59 47 50 45 58 56 44 00 583.0 855 00 64 62 63 65 00 565.8 236 00 68 75 70 71 28 69 29 00 478.2 997 00 73 76 33 35 34 32 00 316.5 988 00 78 67 16 15 13 11 00 225.0 429 00 77 00 136.5 9410 00 10 17 18 21 20 00 149.6 6311 00 22 00 140.4 3712 00 30 00 146.7 8113 00 24 00 138.9 9214 00 25 26 27 23 74 00 175.4 8615 00 19 66 14 12 00 115.2 9816 00 31 09 00 74.6 3817 00 08 06 00 49.6 9918 00 07 79 00 49.3 6319 00 72 00 3.9 8320 00 22 00 140.4 10021 00 25 00 138.9 10022 00 25 00 138.9 10023 00 25 00 138.9 10024 00 25 00 138.9 10025 00 27 00 97.1 10026 00 27 00 97.1 10027 00 32 00 241.1 10028 00 34 00 270.6 10029 00 53 00 101.1 10030 00 53 00 101.1 10031 00 53 00 101.1 10032 00 70 00 333.9 10033 00 74 00 111.4 10034 00 74 00 111.4 100

Table A3VRP routes generated for HRC.

Route number Routes Distance (km) Capacity utilization (%)

1 00 09 00 25.9 432 00 10 01 23 02 00 165.2 813 00 20 00 175.8 884 00 19 00 175.8 725 00 18 08 17 00 250.7 836 00 03 21 22 15 14 00 416.8 747 00 12 13 06 16 04 11 05 07 00 452.2 868 00 10 00 31.7 1009 00 20 00 175.8 100

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References

Council of Supply Chain Management Professional, 2008. <http://www.cscmp.org>.Gendreau, M., Hertx, A., Laporte, G., 1992. New insertion and postoptimization for the traveling salesman problem. Operations Research 40, 1086–1094.Gendreau, M., Hertx, A., Laporte, G., 1994. A Tabu search heuristic for the vehicle routing problem. Management Science 40, 1276–1290.Ji, P., Chen, K., 2007. The vehicle routing problem: the case of the Hong Kong postal service. Transportation Planning and Technology 30, 167–182.Kim, H.-S., Ryu, J.-H., Hong, M.-S., Rim, S.-C., 2007. Optimizing reverse logistics network for end-of-life electrical appliances. IE Interfaces 20, 154–161.Korean Association of Electronics Environment, 2006. A Study of Recovery and Recycle of End-of-Life Electrical Appliances, Seoul, Korean Association of

Electronics Environment.Korean Association of Electronics Environment, 2007. An Empirical Study on Reverse Logistics of End-of-Life Electrical Appliances. Seoul, Korean Association

of Electronics Environment.Laporte, G., Genreau, M., Potvin, J.-Y., Semet, F., 2000. Classical and modern heuristics for the vehicle routing problem. International Transactions in

Operational Research 7, 285–300.Miller, R.E., Thatcher, J.W., 1972. Complexity of Computer Computations. Plenum Press, New York.Moritz, H., 1980. Geodetic reference system. Journal of Geodesy 54, 1980.Schultmann, F., Zumkeller, M., Rentz, O., 2006. Modeling reverse logistic tasks within closed-loop supply chains: an example from the automotive industry.

European Journal of Operational Research 171, 1033–1050.Stock, J.R., 1992. Reverse Logistics. Council of Logistics Management, Oak Brook.