應用基因演算法求解有限容量多種交通工具及多時窗之運輸模式

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研究生：	邱玉純 Yu-Chun Chiu
論文名稱：	應用基因演算法求解有限容量多種交通工具及多時窗之運輸模式 A Study on Using Genetic Algorithm to Solve a Vehicle Routing Model with Multiple Vehicle Types and Multiple Time Windows
指導教授：	王小璠 Hsiao-Fan Wang
口試委員:
學位類別：	碩士 Master
系所名稱：	工學院 - 工業工程與工程管理學系 Department of Industrial Engineering and Engineering Management
論文出版年：	2006
畢業學年度：	94
語文別：	英文
論文頁數：	77
中文關鍵詞：	車輛巡迴路徑及時程問題、多種類運輸工具、多時窗、基因演算法
外文關鍵詞：	Vehicle Routing and Scheduling Problem, Multiple Vehicle Types, Multiple Time Windows, Genetic Algorithm
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自從Dantzig and Ramser　在1959年提出第一個Vehicle Routing Problem (VRP)的問題之後，有許多的學者相繼提出不同的運輸模式去解決實際的車輛巡迴問題。
　　由於運輸問題不僅攸關一公司的營運成本，同時維繫與顧客之信譽。因此，如何設計出適合實際運作的運輸模式以及如何規劃好運送路線來滿足所有的顧客是我們研究的主要考量。在本篇論文中，我們所考慮的運輸問題涵蓋三方面：第一是從多種類的運輸工具派出最適切的運輸工具去服務一組顧客群；第二是如何安排路徑，使運輸工具的運輸路徑為最短；其三是在多時窗的模式中，如何有效率地決定顧客和顧客之間的時窗組合搭配以滿足顧客的需要。因此，所建立的運輸模式，主要的目標是幫助營運者在滿足所有的顧客時窗與服務量的要求下決定ㄧ運輸工具的派遣成本與路徑安排的相關作業成本最小的巡迴路徑。
　　由於此運輸模式是屬於NP-hard的問題，因此，在本研究中設計一有效率的基因演算法來求解我們的問題。經驗證結果顯示，所設計發展的演算法：一方面，在顧客數較少時使用基因演算法可以確切地得到最佳解；另一方面，在顧客數較多時使用基因演算法也可以得到近似最優解且求解的時間優於一般常用求解線性規劃問題的專業軟體，例如：LINGO 9.0以及CPLEX 9.0。故研究結果能有效地提供物流中心有利的運輸車輛巡迴之相關作業應用。最後，我們在結論中總結出本論文的重點及未來相關的研究方向。

In 1959 Dantzig and Ramser proposed a Vehicle Routing Problem (VRP). Since then developed and extended to varieties of problems in order to reflect contemporary requirements, distributing goods is a part of our daily life. No matter it is an overseas letter sent via FedEx, or a local product delivered by TAKKYUBIN, they all belong to distribution domain. Therefore, commodity distribution occurs anytime, everywhere, among countries, companies, and individuals.
This study is focused on vehicle routing and scheduling problems, with different features considered for more realistic applications. These features include: multiple vehicle types for companies and multiple time windows for customers. In addition, to assist a logistic company in distributing goods effectively, the distribution costs that need to be minimized include vehicle dispatching cost, traveling cost, and time-window combination cost. In practice, the complexity of the problem makes it necessary to develop a structural model for facilitating a general analysis and applications. Such model has been developed and illustrated by two examples.
Because the considered vehicle routing and scheduling problem with multiple vehicle types and multiple time windows (VRSP-MVMT) is a nondeterministic polynomial time (NP)-hard problem, we have developed a genetic algorithm (GA) for efficient solution. On one hand, when the problem is small-scale problem, the developed GA has shown to be capable of obtaining the optimal solution; on the other hand, when the scale of the problem is large, the GA can obtain the near-optimal solution. Finally, we will draw our conclusion regarding our study and future research.

TABLE OF CONTENTS
ACKNOWLEDGEMENT    VI
中　文　摘　要    VII
ABSTRACT    VIII
NOTATIONS    IX
CHAPTER 1 INTRODUCTION    1
1.1 Background    1
1.2 Motivation and Purposes of Study    2
1.3 Framework of the Study    3
1.4 Summary    5
CHAPTER 2 LITERATURE REVIEW    6
2.1 Vehicle Routing Problems (VRP)    6
2.1.1 Definition and Classification    6
2.1.2 Properties of Vehicle Routing Problems    9
2.1.3 A Basic Mathematical Formulation of VRP    12
2.1.4 Solution of Vehicle Routing Problems    13
2.2 Vehicle Scheduling Problems (VSP)    15
2.3 Vehicle Routing and Scheduling Problems (VRSP)    21
2.4 Extensions of VRSP    23
2.4.1 Splitting Delivery Routing    23
2.4.2 Time Windows    24
2.5 Solution Procedure: Meta Heuristic Algorithm    25
2.5.1 Genetic Algorithm    25
2.5.2 Tabu Search    27
2.5.3 Comparison of These Two Meta-heuristics    28
2.6 Discussion and Summary    29
CHAPTER 3 THE PROPOSED MODEL    30
3.1 Notations    31
3.2 Model for VRSP-MVMT    32
3.3 Linearization of VRSP-MVMT    35
3.4 Properties of VRSP-MVMT    37
3.5 Illustrative Example I    38
3.6 Illustrative Example II    41
3.7 Comparison and Discussion    43
3.8 Summary    45
CHAPTER 4 A GENETIC ALGORITHM for VRSP-MVMT    46
4.1 The Proposed GA    46
4.1.1 The Structure of the Algorithm    46
4.1.2 Illustrative Procedure    48
4.2 Evaluation    56
4.2.1 Evaluation of Accuracy    58
4.2.2 Evaluation of Efficiency    59
4.3 Summary    60
CHAPTER 5 CONCLUSION    62
REFERENCES    63
APPENDIX I GA PROCEDURE    68
APPENDIX II BENCHMARK DATA R205    72
FIGURES CAPTIONS
Fig. 1.1 Research Framework of VRSP-MVMT    4
Fig. 2.1 A Vehicle Routing Problem    7
Fig. 2.2 A Multiple Depot Problem    8
Fig. 2.3 Vehicle Scheduling with No Path Length Restrictions    18
Fig. 2.4 Vehicle Scheduling with 2 Vehicle Types    19
Fig. 2.5 Vehicle Routing with 2 Depots    20
Fig. 4.1 The Structure of the Proposed GA    48
Fig. 4.2 The Encoding Method    50
Fig. 4.3 Two Mutation Methods    51
Fig. 4.4 Time Windows Selection    55

TABLES CAPTIONS
Table 2.1 General Characteristics of Routing Problem[10]    9
Table 2.2 Constraints of Routing Configuration [10]    11
Table 2.3 Summary of Solution Strategies in Vehicle Routing[10]    14
Table 2.4 Characteristics of Routing and Scheduling Problems    21
Table 2.5 Crossover Examples    26
Table 2.6 Mutation Examples    27
Table 2.7 Comparison of TS,GA and Exact Procedure    28
Table 3.1 Locations of All Nodes    39
Table 3.2 Traveling Cost,   (unit: dollars)    39
Table 3.3 Traveling Time,  (unit: minutes)    39
Table 3.4 Solution of Example I    41
Table 3.5.1 Solution of Example II    43
Table 3.5.2 Solution of Example II    43
Table 3.6 Comparison of Three Models    44
Table 4.1 One Cut-point Method    51
Table 4.2 Randomly Mutate One Gene for Chromosomes    52
Table 4.3 Swap Time Windows    52
Table 4.4 The Decoding Method    53
Table 4.5 Rule 1: Exact Services    54
Table 4.6 Rule 2: Demand cannot Exceed Capacity    54
Table 4.7 The Remaining 5 Customers for 10 Customers    58
Table 4.8 Comparison of Optimal Values and Best Values    59
Table 4.9 Comparison of Feasible Solutions and Best Values    60

                                

1. REFERENCES清華大學作業研究教材編寫組，作業研究，儒林圖書公司，1992
2. 許秋文，「應用行產生法求解多場站多趟次車輛巡迴路徑問題之研究」，清華大學工業工程與工程管理學系碩士論文，1995
3. Ali A. and J. Kennington, “The asymmetric M-traveling salesman problem: a duality based branch and bound”, Discrete Applied Mathematics 13, 259-276 (1986)
4. Assad A.A. and B.L. Golden, Vehicle Routing: Methods and Studies, Elsevier, New York (1998)
5. Baker, B.M. and M.A. Ayechew, “A genetic algorithm for the vehicle routing problem”, Computers & Operations Research 30, 787-800 (2003)
6. Berger, J and M. Barkaoui, “ A parallel hybrid genetic algorithm for the vehicle routing problem with time windows”, Computers & Operationl Research 31, 2037-2053 (2004)
7. Bertsimas D.,”A vehicle routing problem with stochastic demand”, Operations Research 40, 574-586 (1992)
8. Bertsimas D. and G. V. Ryzin, “A stochastic and dynamic vehicle routing problem in the Euclidean plane”, Operations Research 39, 601-615 (1991)
9. Bertsimas D. and G.V. Ryzin, “Stocastic and dynamic vehicle routing in the Euclidean plane with multiple capacitated vehicle”, Operations Research 41, 60-76 (1993)
10. Bodin L.D., B.L. Golden, A.A. Assad and M.O. Ball, “Routing and scheduling of vehicles and crews”, Computers & Operations Research 10, 63-211 (1983)
11. Bodin L.D. and L. Berman, “Routing and scheduling of school buses by computer” Transportation Science 13, 113-129 (1979)
12. Bodin L.D. and S. Kursh, “A detailed description of a street sweeper routing and scheduling system” Computers & Operations Research 6, 181-198 (1979)
13. Bodin L.D. and S. Kursh, “A computer-assisted system for the routing and scheduling of street sweepers” Operations Research 26, 525-537(1978)
14. Chao I.M., B.L. Golden and E. Wasil, “A new heuristic algorithm for the multi-depot vehicle routing problem that improves upon best-known solutions”, American Journal of Mathematical Management Science 13, 371-406 (1993)
15. Chapleau L., J.Ferland and J.M. Rousseau, Clustering for routing in dense are, University of Montreal Transportation Research Center Publication, NO.206 (1981)
16. Christofides N.,A. Mingozzi and P. Toth, “ State space relaxation procedures for the computation of bounds to routing problems” Networks 11, 145-164 (1981)
17. Christofides N.,A. Mingozzi and P. Toth, “Exact algorithms for the vehicle routing problem, based on spanning tree and shortest path relaxations” Mathematical Programming 20, 255-282 (1981)
18. Clarke G. and J. Wright, “Scheduling of vehicle from a central depot to a number of delivery points” Operations Research 12, 568-581 (1964)
19. Crowder H. and M. Padberg, “Solving large-scale symmetric traveling salesman problems to optimality” Management Science 26, 495-509 (1980)
20. Cullen F., J.Jarvis and H. Ratliff, “Set partitioning based heuristics for interactive routing” Network 11, 125-144 (1981)
21. Dantzig G.B. and J.H. Ramser, “The Truck Dispatching Problem”, Management Science 6, 80-91 (1959)
22. Dror M. and P. Trudeau, “Savings by split delivery routing” Transportation Science 23, 141-149 (1989)
23. Dror M., G. Laporte and P. Trudeau, “Vehicle routing with stochastic demands: properties and solution framework”, Transportation Science 23, 166-176 (1989)
24. Dror M. and P. Trudeau, “Split delivery routing”, Naval Research Logistics 37, 383-402 (1990)
25. Eric T. AND B. Philippe, “ A tabu search heuristic for the vehicle routing problem with soft time windows”, Transportation Science 31, 170-186 (1997)
26. Fabri A. and P. Recht, “On dynamic pickup and delivery vehicle routing with several time windows and waiting times”, Transportation Research ,Part B 40, 335-350 (2006)
27. Fisher M.L. and R. Jaikumer, “ A generalize assignment heuristic for vehicle routing”, Networks 11, 109-124 (1981)
28. Frizzell P.W. and J.W. Giffin, “The split delivery vehicle scheduling problem with time windows and grid network distances”, Computers & Operations Research 22, 655-667 (1995)
29. Gillett B. and J. Johnson, “Multi-terminal vehicle-dispatch algorithm”, Omega 4, 711-718(1976)
30. Gillett B. and L. Miller, “A heuristic algorithm for the vehicle dispatch problem”, Operations Research 22, 340-349 (1974)
31. Glover, F. “ Heuristic for integer programming using surrogate constraints”, Decision Science 8, 156-166 (1977)
32. Golden B., T. Magnanti and H. Nguyen, “Implementing vehicle routing algorithm” Networks 7, 113-148 (1977)
33. Golden B. and R. Wong, “Capacitated arc routing problems” Networks 11, 305-315 (1981)
34. Barbarosoglu G and D. OZgur, “ A tabu search algorithm for the vehicle routing problem” Computers & Operations Research 26, 255-270 (1999)
35. Held M. and R. Karp, “The traveling salesman problem and minimum spanning trees” Operations Research 18, 1138-1162 (1970)
36. Hillier F.S. and G.J. Lieberman, Introduction to Operation Research, 7th Ed., McGRAW-Hill, Dubuque Iowa (2001)
37. Ho S.C. and D. Haugland, “A tabu search heuristic for the vehicle routing problem with time windows and split deliveries”, Computers & Operations Research 31, 1947-1964 (2004)
38. Hong S. and M. W. Padberg, “A note on the symmetric multiple traveling salesman problem with fixed charges”, Operations Research 25, 871-874 (1977)
39. Krolak P, W. Felts and J. Nelson, “A man-machine approach toward solving the generalized truck dispatching problem” Transportation Science 6, 149-170 (1972)
40. Karp R. “Probabilistic analysis of partitioning algorithms for the traveling salesman problem in the plane” , Mathematical Operations Research 2, 209-224 (1977)
41. Lacomme P., C. Prins and M. Sevaux, “ A genetic algorithm for a bi-objective capacitated arc routing problem”, Computers & Operations Research 33, 3473-3485 (2006)
42. Laporte G. and Y. Nobert and S. Taillefer, “Solving a family of multi-depot vehicle routing and location-routing problems”, Transportation Science 22, 161-172 (1988)
43. Lin S. and B. Kerninghan, “ An effective heuristic algorithm for the traveling salesman problem” Operations Research 21, 498-516 (1973)
44. Lin Y. and Y. Zhao, “A new algorithm for the directed Chinese postman problem”, Computers & OperationsResearch 15, 577-584 (1988)
45. Magnanti T. “Combinatorial Optimizations and vehicle fleet planning: perspectives and prospects”, Networks 11, 179-214
46. Marius M. Solomon, “Algorithms for the vehicle routing and scheduling problems　with time window constraints”, Operations Research 35, 254-265 (1987)
47. Marius M. Solomon, “Benchmark Data” http://web.cba.neu.edu/%7Emsolomon/problems.htm
48. Miliotis P. “Using cutting planes to solve the symmetric traveling salesman problem” Mathematical Programming 15, 177-188 (1978)
49. Mitsuo G. and C. Runwei, Genetic Algorithms and Engineering Design, John Wiley & Sons, New York, (1997)
50. Mokhtar S.B.,J.J. Jarvis and H.D. Sherali, Linear programming and network flows, third edition, WILEY (2005)
51. Norback J. and R. Love, “Geometric approaches to solving the traveling salesman problem” Management science 1208-1223 (1977)
52. Orloff C.S., “A fundamental problem in vehicle routing “ Networks 4, 35-64 (1974)
53. Russell R. and W. Igo, “ An Assignment Routing Problem”, Networks 9, 1-17 (1979)
54. Russell, R, “An effective heuristic for the M-tour traveling salesman problem with some side conditions” Operations Research 6, 517-524 (1977)
55. Stern H. and M. Dror, “Routing electric meter readers”, Computers & Operational Research 6, 209-223 (1979)
56. Thomomason P.M. and S. Psaraftis, “ Cyclic transfer algorithms for multi-vehicle routing and scheduling problems”, Operations Research 41, 935-946 (1993)

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