• 沒有找到結果。

A Model for the Implementation of a Two-Shift Municipal Solid Waste and Recyclable Material Collection Plan that Offers Greater Convenience to Residents

N/A
N/A
Protected

Academic year: 2021

Share "A Model for the Implementation of a Two-Shift Municipal Solid Waste and Recyclable Material Collection Plan that Offers Greater Convenience to Residents"

Copied!
9
0
0

加載中.... (立即查看全文)

全文

(1)

This article was downloaded by: [National Chiao Tung University 國立交通大學]

On: 24 April 2014, At: 18:50

Publisher: Taylor & Francis

Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer

House, 37-41 Mortimer Street, London W1T 3JH, UK

Journal of the Air & Waste Management

Association

Publication details, including instructions for authors and subscription information:

http://www.tandfonline.com/loi/uawm20

A Model for the Implementation of a Two-Shift

Municipal Solid Waste and Recyclable Material

Collection Plan that Offers Greater Convenience to

Residents

Hung-Yueh Lin

a

, Zong-Pei Tsai

a

, Guan-Hwa Chen

b

& Jehng-Jung Kao

b a

Department of Environmental Engineering and Management , Chaoyang University

of Technology , Taichung , Taiwan , Republic of China

b

Institute of Environmental Engineering, National Chiao Tung University , Hsinchu ,

Taiwan , Republic of China

Published online: 10 Oct 2011.

To cite this article: Hung-Yueh Lin , Zong-Pei Tsai , Guan-Hwa Chen & Jehng-Jung Kao (2011) A Model for

the Implementation of a Two-Shift Municipal Solid Waste and Recyclable Material Collection Plan that Offers

Greater Convenience to Residents, Journal of the Air & Waste Management Association, 61:1, 55-62, DOI:

10.3155/1047-3289.61.1.55

To link to this article:

http://dx.doi.org/10.3155/1047-3289.61.1.55

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”)

contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors

make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any

purpose of the Content. Any opinions and views expressed in this publication are the opinions and views

of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content

should not be relied upon and should be independently verified with primary sources of information.

Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs,

expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in

connection with, in relation to or arising out of the use of the Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematic

reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any

form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at

http://

www.tandfonline.com/page/terms-and-conditions

(2)

A Model for the Implementation of a Two-Shift Municipal

Solid Waste and Recyclable Material Collection Plan that

Offers Greater Convenience to Residents

Hung-Yueh Lin and Zong-Pei Tsai

Department of Environmental Engineering and Management, Chaoyang University of Technology,

Taichung, Taiwan, Republic of China

Guan-Hwa Chen and Jehng-Jung Kao

Institute of Environmental Engineering, National Chiao Tung University, Hsinchu, Taiwan, Republic

of China

ABSTRACT

Separating recyclables from municipal solid waste (MSW) before collection reduces not only the quantity of MSW that needs to be treated but also the depletion of re-sources. However, the participation of residents is essen-tial for a successful recycling program, and the level of participation usually depends on the degree of conve-nience associated with accessing recycling collection points. The residential accessing convenience (RAC) of a collection plan is determined by the proximity of its col-lection points to all residents and its temporal flexibility in response to resident requirements. The degree of prox-imity to all residents is determined by using a coverage radius that represents the maximum distance residents need to travel to access a recycling point. The temporal flexibility is assessed by the availability of proximal recy-cling points at times suitable to the lifestyles of all resi-dents concerned. In Taiwan, the MSW collection is im-plemented at fixed locations and at fixed times. Residents must deposit their garbage directly into the collection vehicle. To facilitate the assignment of collection vehicles and to encourage residents to thoroughly separate their recyclables, in Taiwan MSW and recyclable materials are usually collected at the same time by different vehicles. A heuristic procedure including an integer programming (IP) model and ant colony optimization (ACO) is explored in this study to determine an efficient two-shift collection plan that takes into account RAC factors. The IP model has been developed to determine convenient collection

points in each shift on the basis of proximity, and then the ACO algorithm is applied to determine the most ef-fective routing plan of each shift. With the use of a case study involving a city in Taiwan, this study has demon-strated that collection plans generated using the above procedure are superior to current collection plans on the basis of proximity and total collection distance.

INTRODUCTION

The traditional treatments of municipal solid waste (MSW), such as landfill and incineration, have become difficult and expensive because of the increasing scarcity of suitable land and associated environmental concerns. Thus, recycling is regarded as one of the major solutions to the challenge of effective waste management. How-ever, the participation of residents is a crucial factor for the achievement of a successful recycling program. Vari-ous researchers1,2 have already acknowledged that the

success of MSW recycling schemes is highly dependent on the participation of residents, which is itself dependent on convenient access to a recycling collection point. The degree of residential accessing convenience (RAC) to a recycling point can be determined based on proximity (location) and temporal flexibility (timetable). A perma-nent recycling point, such as a recycling material broker, can provide a sufficient number of alternative time slots to the residents. By contrast, a temporary recycling point, such as the collection points associated with the tandem collection of MSW and recyclables, does not provide any options in terms of collection times. Therefore, it can be seen that the proximity of collection points and a flexible schedule will affect the level of willingness in residents to participate in the recycling program.

In Taiwan, MSW collection is implemented using fixed locations under fixed time slots, and the residents must deposit their garbage into the collection vehicle. To facilitate the assignment of collection vehicles and to encourage res-idents to separate recyclables properly, in Taiwan MSW and recyclable materials are usually collected at the same time but in different vehicles. It can be seen from this practice that the major goal of MSW collection programs in Taiwan is to “keep trash off the ground.” Such a policy can avoid

IMPLICATIONS

RAC is essential to the achievement of a successful tandem MSW and recyclable material collection plan. This study has proposed an optimization model and a metaheuristic tool to determine the routing plan. Through a real case study, a two-shift routing/scheduling plan is obtained by using the proposed procedure, which is to be superior to the existing collection plan. In short, local authorities that propose to determine a two-shift MSW and recyclable ma-terial collection plan should consider the proposed model as a basis of their operations.

(3)

accumulations of waste on curbsides, which blight the en-vironment, generate bad odors, and attract flies, especially in a tropic area such as Taiwan. Additionally, MSW collec-tion crews can inspect the MSW being disposed of and directly instruct residents, if necessary, to properly separate their recyclables. However, a single fixed collection time is not always convenient to all residents and may require many of them to dispose of their garbage at more distant collection points at a less inconvenient time. Subsequently, such a process of waste collection obviously does not en-courage residents to participate fully in any recycling pro-gram. Fortunately, this situation has been addressed by the implementation of a night-and-day shift collection sched-ule, which is designed to improve the RAC. However, the absence of an existing procedure with which to determine the level of RAC associated with a collection plan for recy-cling makes the identification of superior routing plans difficult.

RAC is mainly determined by the proximity of the collection point and the time of collection. For the deter-mination of recycling collection points, proximity is gen-erally considered and evaluated based on the distance from a residence to the nearest collection point. Several researchers have already proposed alternative approaches for addressing problems concerning proximity. In loca-tion studies of emergency facilities, Toregas and ReVelle3

have proposed a coverage distance of a facility and they have developed a mathematical model to find the mini-mal number of fire stations required to cover the target population. In their definition, the residents within the coverage distance of a facility are regarded as being ser-viced. Kao and Lin,4 from the viewpoint of a resident,

suggest using an acceptable walking distance to evaluate the service level of MSW collection. They have imple-mented four different walking distances (between 50 and 100 m) using the shortest service location model to assess how each of the four distances influences the number of required collection points. Lin and Chen5have also

pro-posed a proximity indicator on the basis of coverage and walking distances to determine the locations of perma-nent recycling depots. In general, the number of required facilities increases as the coverage or walking distance decreases no matter which proximity definition is used. Although proximity is essential for planning a permanent recycling point, it should not be the only factor to con-sider for planning a temporary one because not all resi-dents are able to handle recyclable materials at the same collection time. The other major factor, temporal flexibil-ity (collection times), should also be considered in the determination of temporary recycling collection points.

In addressing temporal problems, methods with time-window constraints, in which each customer is as-signed an acceptable period to be serviced, have been widely explored. For example, Shih and Lin6applied

time-window constraints to develop a routing plan for the collection of infectious waste from several hospitals. Nu-merous other studies7–9 have also applied a similar

ap-proach for vehicle routing problems. However, the appli-cation of time-window constraints to a mathematical model designed to evaluate the temporal flexibility of MSW and recyclable material in tandem collection prob-lems is impractical because of the computational effort

required to solve even a small-scale problem, as well as the fact that the preferred collection times usually differ among neighboring residents. In reality, only two broad collection time options exist; that is, day or night time. For residences with someone at home most of the day, collection during the daytime is preferred, whereas those working during the day would prefer to dispose of their garbage and recyclable material in the evening. Therefore, a two-shift, day and night collection schedule is proposed because of the increased flexibility that it provides for servicing residences. However, it needs to be stated that because of budgetary constraints, not all collection points can be serviced in both shifts. As a response to this bud-getary limitation, this study offers a procedure with which to determine more convenient collection points in each shift on the basis of RAC factors.

Mathematical models such as integer programming (IP) and mixed integer programming (MIP) models are widely applied for various waste management problems, such as in the determination of the route of waste collec-tion services and the locacollec-tions of recycling depots. In MIP models,10 –18the objective function and constraints

incor-porated in each differ from model to model and are de-pendent on the purpose of the model. Badran and El-Haggar19 have summarized some optimization methods

in the field of MSW management. In this study, an IP model is developed to determine the collection shift of preannounced collection points.

Once the collection points of a shift have been iden-tified, the routing plan has to be determined. MIP models (e.g., traveling salesman problem models) can also be applied to optimize the routing of such services and they provide the added advantage of ensuring that the ob-tained solution is the optimal one. However, the length of time required to solve a large MIP model is impractical. Consequently, most heuristic method studies have been devoted to find optimal service routes, such as genetic algorithm,20tabu search,21and ant colony optimization

(ACO).22Among these heuristic methods, the ACO

algo-rithm has been widely adopted in studies concerned with traveling salesmen problems because it is capable of find-ing sound solutions within an acceptable time frame.23,24

Briefly, an ACO is a heuristic algorithm that simulates the behaviors of ants in their search for food. In this study, an ACO is implemented to determine the schedule of a day and night (two-shift) waste collection service.

THE ANALYTICAL PROCESS

Figure 1 presents the procedure to determine a two-shift MSW collection routing plan. First, the data relating to MSW collection points, population distribution, and MSW generation are collected. Then, the regions eligible for a two-shift collection schedule and the coverage radius of each collection point are delineated. Alternative acces-sible collection points (AACPs) are identified based on the coverage radius. An IP model is then implemented to classify the collection points into day and night shifts. Finally, the collection points of each shift form a typical routing problem that is solved using an ACO algorithm to determine the two-shift MSW collection routing plan. The planner can also adjust the parameters, including the

Lin et al.

(4)

regions for two shifts and the coverage radius, to recon-sider other alternative routing plans if desired. Each step of the procedure is described in detail below.

Regions in Need of a Two-Shift Collection Routing Plan

In contrast to a highly populated region with varied work schedules for different residents that can economically jus-tify a two-shift collection schedule, other regions may not require the same or be cost-effective enough for its provi-sion. For instance, an area with mostly office and commer-cial buildings may be empty after office hours, and a rural area is not cost-effective enough to justify the implementa-tion of a two-shift MSW collecimplementa-tion schedule because its residents are sparsely distributed. Therefore, the suitability of an area to a two-shift collection plan should be evaluated carefully. Generally, there would be no need to implement a two-shift collection plan for commercial and rural areas.

Coverage Radius and AACPs

These recycling collection points in the one-shift collec-tion plan are supposed to be conveniently located for the targeted residents. In the two-shift collection plan, resi-dents unable to access their nearest collection point at the

scheduled collection time can dispose of their recyclables at one of the alternative collection points in the other shift. The proximity of an alternative collection point can be evaluated by measuring the distance from the nearest collection point to a potential alternative recycling point. In this study, the coverage radius represents the maxi-mum distance that residents have to walk from the near-est collection point to an alternative collection point. An AACP for a collection point is one for which the distance is less than the coverage radius. Figure 2 illustrates the relationship between a collection point (point 1), cover-age radius (R), and the AACPs of the collection point (i.e., points 3 and 4). Residents can access the nearest collec-tion point or its AACPs. The value of R is the indicator to assess the proximity of the AACPs. A numerically small value for R indicates the collection point and its AACPs are close, which implies the expected distances for nearby residents to access the AACPs are short and vice versa.

The Proposed IP Model

An IP model is established to identify the minimal num-ber of collection points required within a predefined cov-erage radius and areas for a two-shift collection schedule, as formulated below.

Figure 1. The proposed procedure for developing a two-shift MSW and recycling collection plan.

(5)

Min

i⫽1 M

j⫽ 1 T xi,j (1) subject to xi,j⫹

k⑀Ni xk,jⱖ 1 ᭙ i,j (2)

j⫽ 1 T xi,jⱖ 1 ᭙ i (3)

where i and k are the indices of the collection points, M is the total number of collection points, j is the index of a shift, T is the total number of shifts, xi,j is a binary

variable for which the value is equal to 1 if collection point i is part of shift j, and Niis the set of the AACPs of

collection point i.

Equation 1 is the objective function of the proposed IP model that is used to minimize the total sum of the respective number of selected collection points in each shift. Equation 2 ensures that a collection point itself or one of its AACPs is selected in each shift. Equation 3 ensures that all collection points are visited at least once among all shifts. The result after applying the proposed IP model (i.e., the collection points selected for each shift) is analyzed in the next step to find the routing plan of each shift.

An ACO Algorithm-Based Routing Plan

In this stage, a collection routing schedule for each shift is determined using an ACO algorithm. As mentioned pre-viously, the ACO is an algorithm that models the behav-ior of ants in search of the shortest path from their for-micary to the food source.22In the initial stage, ants move

out from their formicary and take random paths to the food source. They also lay down pheromones during their movements to attract other ants to follow their pioneer paths. Because the speed at which ants move is consistent,

after a while the shorter paths (from formicary to food source) will accumulate more pheromones than the longer ones. Moreover, because pheromones decay with time, fewer and fewer ants are attracted to follow a path with a lower pheromone concentration (a long distance path), and this also causes a further decrease of phero-mones on the longer paths. In short, over time, the ants will consistently follow the shortest path from the formi-cary to a food source.

The procedure to implement the ACO algorithm is briefly described below, along with a detailed reference to Dorigo and Caro.22 Artificial ants in the ACO

algo-rithm simulate the actions that real ants display in their search for food. In the preliminary application of the ACO algorithm to a collection routing problem, each artificial ant starts at a random collection point and keeps moving to an unvisited collection point accord-ing to a probability rule until all points are traversed. The probability rule confines artificial ants to choosing the next point with a higher pheromone level and shorter distance. Additional deposits of pheromones are made after the ant finishes the complete tour or crosses any edge.

CASE STUDY

Study Area

To demonstrate the applicability of the proposed IP model and ACO algorithm, a case study is conducted. Taichung City is the third largest metropolis in Taiwan. Its area is approximately 163 km2 with more than 1

million inhabitants. Nantun District is one of its eight major administrative districts. Figure 3 shows the loca-tions of the Nantun District in Taichung City and the city in relationship to the rest of Taiwan. The district itself has a population of approximately 150,000 that generated an average MSW of 141 t/day in 2008.20 In

total there are 1289 MSW collection points in the dis-trict. All of these points are serviced during the day, whereas 136 points are also serviced during a night shift. Residents can dispose of their garbage and recy-clable materials in both shifts. Figure 4 illustrates the distribution of the collection points in the district. Open circles represent day-shift only collection points, and solid circles represent those serviced at both shifts.

Figure 2. AACPs.

Figure 3. Location of the studied case in Taichung City, Taiwan.

Lin et al.

(6)

The Areas in Which a Two-Shift Collection Schedule Operates

In accordance with the procedure shown in Figure 1, the required data are collected first. To identify appropriate areas in which to implement a two-shift collection sched-ule, the population distribution of each area is estab-lished. Figure 5a represents the population density of Nantun District, which is determined from census data and the geographical information map layers of house-hold address locations. Figure 5b illustrates five groups, A to E, which are ranked in order of descending population densities. For example, group A is the highest populated group of subareas in Nantun District with 50% of the total population, group B has 13% of the total population of the district, and so on. Five scenarios are analyzed in this study. Table 1 lists the area groups included in each sce-nario, indexed from I to V. Each scenario includes differ-ent subareas for which a two-shift collection schedule is being considered.

Coverage Radius and Respective AACPs

Kao and Lin4have suggested that for residents the

appro-priate walking distance to a collection point is less than 100 m. For a comparison using the coverage radius of an existing two-shift collection plan, an extraordinary long distance of 500 m is also evaluated. Thereby, the coverage radii analyzed in this study are 50, 75, 100, and 500 m. The AACPs under different coverage radii are determined and the proposed IP model is applied to generate the collection points for each shift of a two-shift collection routing plan for each scenario.

Determination of ACO Parameters

A two-stage test was conducted to ensure the applicability of ACO in the identification of the optimal solution and to determine the parameters for applying the ACO algo-rithm. The ACO tool used in this study is ACOTSP.25In

the first stage, several hypothetical cases with 30, 40, 50, and 60 collection points were randomly created and they were resolved using CPLEX26(an optimization tool) and

ACOTSP separately. The test results indicate that the best solution of the five ACOTSP test runs is identical to the solution gained from CPLEX for all tested cases, which

establishes the superiority of ACO algorithms in the iden-tification of the optimal routes. In the second stage, the ACO parameters for the case study were determined, in-cluding the number of ants (m), the pheromone decay coefficient (␣), and the relative importance of exploita-tion versus exploraexploita-tion on the movements of the ants (q0). Table 2 lists the tested values of the parameters. Each

combination of parameter values (e.g., 2 for m, 0.1 for␣, and the 0.85 for q0) is tested five times in this case study

using ACOTSP. For comparison, the average performance of a parameter value is to calculate all combinations with the identical parameter value by using the following equation: P⫽ 1 ⫺

t⫽ 1 C Dt C ⫺ D* D* (4)

where P is the average performance of a parameter value,

C is the number of combinations with the identical

pa-rameter value, Dtis the average solution of the tth

com-bination for the parameter value, and D* is the global

Figure 4. The MSW and recycling in tandem collection points of Nantun District.

Figure 5. (a) The population density distribution of Nantun District, and (b) the subareas classified by percentage of population from high to low population density.

(7)

optimal solution of all of the tested runs. For instance, the average performance percentage of ␣ ⫽ 0.3 is 99.66%, where m and q0of combinations of this group varies from

2 to 15 and 0.85 to 1, respectively. As listed in Table 2, the minimal and maximal values of P are 99.49% for m⫽ 2 and 99.76% for m⫽ 3. This study found ACO to be highly effective in identifying the best solution to the problem set. The numerals underlined in Table 2 are the parameter values used in the following scenario analysis.

RESULTS AND DISCUSSION

The scenarios analyzed with different coverage radii are denoted by Sy.x, where y is the scenario index, as indi-cated in Figure 5b and Table 1, and x represents the value of a coverage radius, as listed in Table 3. The coverage radius implies the maximal distance for a resident to walk to access the AACP. The current collection plan is denoted by R.now. In addition, a scenario denoted by R.aco that optimizes the routing plan in each shift of R.now by the ACO algorithm is also implemented.

Table 3 summarizes the results obtained for all of the analyzed scenarios using the proposed method, including the number of collection points in day and night shifts and the total collection distance of each scenario. Figure 6 compares the results for total collection distances versus coverage radii. For comparison, the values of the coverage radius are expressed by their logarithmic values. Obvi-ously, the total collection distance decreases as the cov-erage radius increases, or as the number of subregions

with a two-shift collection schedule decreases. As shown in this figure, the influence of a coverage radius on the total collection distance is less sensitive as it increases because for any given collection point it is easier to find AACPs with a large coverage radius than those with a small one.

Figure 6 also compares the results with the current collection plan, R.now, with its enhanced routing plan,

R.aco. The coverage radius (786 m) of these two plans is

identical, which can be computed by a max-minimal pro-cedure; that is, by calculating the distance between each collection point of one shift and its nearest AACP in the other shift and then by finding the maximum among these distances. The R.now and R.aco plans implement the two-shift collection schedule for the entire area, as for scenario V. To improve the readability of Figure 6, two horizontal lines and one vertical line have been added to highlight the total collection distances and coverage radii of both plans. The gap between the two horizontal lines indicates the improvement in the total collection distance after modifying the routing plan of R.now to R.aco. The modification has reduced the original total collection dis-tance by 26% through the use of the ACO algorithm. In addition, the horizontal lines also provide references for the decision-makers during the evaluation of a new rout-ing plan with various coverage radii, regions with two-shift collection schedules, and collection distances. The vertical line in Figure 6 highlights the coverage radius of 786 m for R.now and R.aco, which is greater than the maximal coverage radius of 500 m for S.# scenarios. This also means all of the routing plans of S.# scenarios offer the residents better proximity than R.now under two-shift collection. In addition, the total collection distances of all of the scenarios are less than that for R.now, which indi-cates these scenarios can be alternatives to replace R.now, the current collection plan. If the collection points in each shift remain unchanged, R.aco is recommended. Al-ternatively, the collection authority or manager may eval-uate the coverage radius, the total collection distance, and the areas eligible for a two-shift collection schedule to find a preferred alternative. For example, if the decision-maker desires to improve the convenience of resident access to recycling as much as possible and the total collection distance of the alternative is less than that of the current collection plan, then S.V.50 is recommended. If a total collection distance of less than S.V.50 is desired, S.IV.50 or S.V.75 is the recommended alternative. S.IV.50 has the most convenient coverage radius (50 m) and of-fers 88% of the population convenient access to a two-shift collection point schedule, whereas S.V.75 offers a moderately convenient walking distance of 75 m and the

Table 1. Populations of subarea groups in Nantun District. Scenario Index Subarea Groups Percentage of Total Population (%) Number of Collection Points Area (m2) I A 50 615 195,811 II A, B 63 792 414,825 III A, B, C 75 1091 744,507 IV A, B, C, D 88 1176 1,338,863 V A, B, C, D, E 100 1289 58,042,577

Table 2. The results for testing ACO parameters. Parameter Value P (%) m 2 99.49 3 99.76 5 99.75 10 99.64 15 99.63 ␣ 0.1 99.65 0.3 99.66 0.5 99.66 0.9 99.65 q0 0.85 99.60 0.9 99.66 0.95 99.72 0.99 99.70 1 99.58

Lin et al.

(8)

entire area can be covered by a two-shift collection point schedule.

CONCLUSIONS

In this study, a heuristic procedure has been proposed to evaluate the RAC level for recyclable material collec-tion points serviced by a two-shift colleccollec-tion plan. The implementation of a two-shift collection schedule and coverage radius of collection points can greatly affect the RAC level to these services. The proposed IP model is used to classify the collection points into two shifts and to satisfy the requirements of AACPs in two-shift collection areas. The ACO is capable of efficiently de-termining the routing plans. The case study presented demonstrates how the proposed method can be applied to a real problem. On the basis of various coverage radii and areas for two-shift collections, different routing plans were analyzed. The results are expected to assist

the local authority in determining a proper two-shift collection plan. In short, the proposed methodology has been demonstrated to be flexible and efficient in the analyses of various scenarios and for the implemen-tation of an improved routing plan.

ACKNOWLEDGMENTS

The authors thank the National Science Council of Taiwan of the Republic of China for financially support-ing this research under contract no. NSC 98-2211-E-324-001-MY2. The authors also express special thanks to Dr. Thomas Stu¨ tzle for his development of ACOTSP and assistance in helping the authors use the program in this study.

REFERENCES

1. McDonald, S.; Ball, R. Public Participation in Plastics Recycling Schemes; Resour. Conserv. Recycl. 1998, 22, 123-141.

2. Tilman, C.; Sandhu, R. A Model Recycling Program for Alabama; Resour. Conserv. Recycl. 1998, 24, 183-190.

3. Toregas, C.; ReVelle, C. Optimal Location under Time or Distance Constraints; Papers Regional Sci. 1972, 28, 131-143.

4. Kao, J.-J.; Lin, T.-I. Shortest Service Location Model for Planning Waste Pickup Locations; J. Air & Waste Manage. Assoc. 2002, 52, 585-592. 5. Lin, H.-Y.; Chen, G.-H. Regional Optimization Model for Locating

Supplemental Recycling Depots; Waste Manage. 2009, 29, 1473-1479. 6. Shih, L.-H.; Lin, Y.-T. Optimal Routing for Infectious Waste

Collec-tion; J. Environ. Eng. ASCE 1999, 125, 479-484.

7. Kim, B.-I.; Kim, S.; Sahoo, S. Waste Collection Vehicle Routing Prob-lem with Time Windows; Comput. Oper. Res. 2006, 33, 3624-3642. 8. Tung, D.V.; Pinnoi, A. Vehicle Routing-Scheduling for Waste

Collec-tion in Hanoi; Eur. J. Oper. Res. 2000, 125, 449-468.

9. Hong, S.-C.; Park, Y.-B. A Heuristic for Bi-Objective Vehicle Routing with Time Window Constraints; Int. J. Prod. Econ. 1999, 62, 249-258. 10. Chang, N.-B.; Yang, Y.C.; Wang, S.F. Solid-Waste Management System Analysis with Noise Control and Traffic Congestion Limitations; J. Environ. Eng. ASCE 1996, 122, 122-131.

11. Sahoo, S.; Kim, S.; Kim, B.-I.; Kraas, B.; Popov, A., Jr. Routing Optimi-zation for Waste Management; Interfaces 2005, 35, 24-36.

12. Baptista, S.; Oliveira, R.C.; Zu´quete, E. A Period Vehicle Routing Case Study; Eur. J. Oper. Res. 2002, 139, 220-229.

Figure 6. The results of the analyzed scenarios.

Table 3. The results for analyzed scenarios. Scenario

ID

Number of Collection Points by Day Shift (number of points)

Number of Collection Points by Night Shift (number of points)

Total Collection Distance of Two Shifts (m) R.now 1,289 136 178,431 R.aco 1,289 136 131,280 S.I.50 1,097 394 133,676 S.I.75 1,042 295 126,840 S.I.100 1,034 264 123,027 S.I.500 1,244 36 112,927 S.II.50 1,041 514 140,827 S.II.75 977 397 133,181 S.II.100 981 343 129,169 S.II.500 1,231 56 115,139 S.III.50 948 714 158,840 S.III.75 878 546 148,440 S.III.100 885 463 143,146 S.III.500 1,213 76 121,235 S.IV.50 934 782 164,841 S.IV.75 857 595 153,532 S.IV.100 861 508 148,303 S.IV.500 1,186 103 122,909 S.V.50 844 898 175,996 S.V.75 756 697 163,689 S.V.100 765 604 155,580 S.V.500 1,179 110 127,389

(9)

13. Kao, J.-J.; Wen, L.-M.; Liu, K.-H. Service Distance and Ratio-Based Location-Allocation Models for Siting Recycling Depots; J. Environ. Eng. ASCE 2010, 136, 444-450.

14. He, L.; Huang, G.H.; Zeng, G.; Lu, H. An Interval Mixed-Integer Semi-Infinite Programming Method for Municipal Solid Waste Manage-ment; J. Air & Waste Manage. Assoc. 2009, 59, 236-246; doi: 10.3155/ 1047-3289.59.2.236.

15. Li, Y.; Huang G. Dynamic Analysis for Solid Waste Management Systems: An Inexact Multistage Integer Programming Approach; J. Air & Waste Manage. Assoc. 2009, 59, 279-292; doi: 10.3155/1047-3289.59.3.279.

16. Lu, H.W.; Huang, G.H.; Liu, Z.F. Greenhouse Gas Mitigation-Induced Rough-Interval Programming for Municipal Solid Waste Management; J. Air & Waste Manage. Assoc. 2008, 58, 1546-1559; doi: 10.3155/1047-3289.58.12.1546.

17. Wang C.; Lin M.-D.; Lin C. Factors Influencing Regional Municipal Solid Waste Management Strategies; J. Air & Waste Manage. Assoc.

2008, 58, 957-964; doi: 10.3155/1047-3289.58.7.957.

18. Lin H.-Y.; Kao J.-J. Subregion Districting Analysis for Municipal Solid Waste Collection Privatization; J. Air & Waste Manage. Assoc. 2008, 58, 104-111; doi: 10.3155/1047-3289.58.1.104.

19. Badran, M.F.; El-Haggar, S.M. Optimization of Municipal Solid Waste Management in Port Said, Egypt; Waste Manage. 2006, 26, 534-545. 20. Holland, J.H. Adaptation in Natural and Artificial Systems; University of

Michigan: Ann Arbor, MI, 1975.

21. Glover, F. Tabu Search—Part I; ORSA J. Comput. 1989, 1, 190-206. 22. Dorigo, M.; Caro, G.D. In New Ideas in Optimization; McGraw-Hill: New

York, 1999; pp 11-32.

23. Montemanni, R.; Gambardella, L.M.; Rizzoli, A.E.; Donati, A.V. A New Algorithm for a Dynamic Vehicle Routing Problem Based on Ant Colony System; Proc. Odysseus 2003, 2, 27-30.

24. Zhang, Y.; Pei, Z.-L.; Yang, J.-H.; Liang, Y.-C. An Improved Ant Colony Optimization Algorithm Based on Route Optimization and Its Appli-cations in Traveling Salesman Problem, Bioinformatics and Bioengi-neering, In Proceedings of the 7th Institute of Electrical and Electronics Engineers International Conference, Boston, MA, 2007; pp 693-698. 25. Stu¨tzle, T. ACOTSP, Version 1.0; available at

http://www.aco-metaheuris-tic.org/aco-code (accessed 2010).

26. Using the CPLEX Callable Library; ILOG: Incline Villiage, NV, 1997.

About the Authors

Hung-Yueh Lin is an associate professor and Zong-Pei Tsai is a graduate student at the Department of Environ-mental Engineering and Management at Chaoyang Uni-versity of Technology in Taichung, Taiwan, Republic of China. Guan-Hwa Chen is a graduate student and Jehng-Jung Kao is a professor at the Institute of Environmental Engineering at the National Chiao Tung University in Hsinchu, Taiwan, Republic of China. Please address cor-respondence to: Hung-Yueh Lin, Department of Environ-mental Engineering and Management, Chaoyang Univer-sity of Technology, 168 Jifong East Road, Wufong Township, Taichung County, 41349, Taiwan, Republic of China; phone: 23323000-4513; fax: ⫹886-4-23742365; e-mail: hylin@cyut.edu.tw.

Lin et al.

數據

Figure 1. The proposed procedure for developing a two-shift MSW and recycling collection plan.
Figure 3. Location of the studied case in Taichung City, Taiwan.
Figure 5. (a) The population density distribution of Nantun District, and (b) the subareas classified by percentage of population from high to low population density.
Figure 6 also compares the results with the current collection plan, R.now, with its enhanced routing plan,
+2

參考文獻

相關文件

Methods include the implementation of waste management plan, reducing the generation at source, charging on disposal of construction waste, recycling of inert hard

The remaining positions contain //the rest of the original array elements //the rest of the original array elements.

Type case as pattern matching on values Type safe dynamic value (existential types).. How can we

在雲中街文創聚落中營運中的「凹凸 咖啡館」是利用當時遺留下的建築群

Based on the observations and data collection of the case project in the past three years, the critical management issues for the implementation of

Therefore, a study of the material (EPI) re-issued MO model for an insufficient output of the LED chip manufacturing plant is proposed in this paper.. Three material

The proposed SEFM method can extract two types of important parameters from the solid model of a construction technology: (1) geometric parameters that specify the dimension

Moreover, due to the firm offers fixed years condition, we focus on this condition to introduce a two-stage game theoretical model which explicitly deals with