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Generalized Vehicle Routing Problem for Reverse Logistics Aiming at Low Carbon Transportation
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 Title & Authors
Generalized Vehicle Routing Problem for Reverse Logistics Aiming at Low Carbon Transportation
Shimizu, Yoshiaki; Sakaguchi, Tatsuhiko;
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 Abstract
Deployment of green transportation in reverse logistics is a key issue for low carbon technologies. To cope with such logistic innovation, this paper proposes a hybrid approach to solve practical vehicle routing problem (VRP) of pickup type that is common when considering the reverse logistics. Noticing that transportation cost depends not only on distance traveled but also on weight loaded, we propose a hierarchical procedure that can design an economically efficient reverse logistics network even when the scale of the problem becomes very large. Since environmental concerns are of growing importance in the reverse logistics field, we need to reveal some prospects that can reduce emissions from the economically optimized VRP in the same framework. In order to cope with manifold circumstances, the above idea has been deployed by extending the Weber model to the generalized Weber model and to the case with an intermediate destination. Numerical experiments are carried out to validate the effectiveness of the proposed approach and to explore the prospects for future green reverse logistics.
 Keywords
Vehicle Routing Problem;Low Carbon Logistics;Hybrid Metaheuristic Method;Modified Saving Method;
 Language
English
 Cited by
1.
Multi-objective optimization for creating a low-carbon logistics system through community-based action, Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2015, 9, 5, JAMDSM0063  crossref(new windwow)
 References
1.
Albareda-Sambola, M., Diaz, J. A., and Fernandez, E. (2005), A compact model and tight bounds for a combined location-routing problem, Computers and Operations Research, 32(3), 407-428. crossref(new window)

2.
Bou, T. and Arakawa, M. (2010), Proposal of a hybrid search method for the bi-objective logistics problem of minimizing total distance traveled and average completion time, Journal of Japan Industrial Management Association, 61(4), 223-233.

3.
Chen, S. Imai, A. and Zhao, B. (2005), A SA-based heuristic for the multi-depot vehicle routing problem, Japan Institute of Navigation, 113, 209-216.

4.
Crevier, B., Cordeau, J. F., and Laporte, G. (2007), The multi-depot vehicle routing problem with inter-depot routes, European Journal of Operational Research, 176(2), 756-773. crossref(new window)

5.
Geiger, M. J. (2010), Fast approximation heuristics for multi-objective vehicle routing problems, Applications of Evolutionary Computation, Lecture Notes in Computer Science, 6025, 441-450.

6.
Gribkovskaia, I., Halskau, O., Laporte, G., and Vlcek, M. (2007), General solutions to the single vehicle routing problem with pickups and deliveries, European Journal of Operational Research, 180(2), 568-584. crossref(new window)

7.
Hashimoto, H., Ibaraki, T., Imahori, S., and Yagiura, M. (2006), The vehicle routing problem with flexible time windows and traveling times, Discrete Applied Mathematics, 154(16), 2271-2290. crossref(new window)

8.
Jozefowiez, N., Semet, F., and Talbi, E G. (2008), Multi-objective vehicle routing problems, European Journal of Operational Research, 189(2), 293-309. crossref(new window)

9.
Kubiak, M. and Wesolek, P. (2007), Accelerating local search in a memetic algorithm for the capacitated vehicle routing problem, Evolutionary Computation in Combinatorial Optimization, Lecture Notes in Computer Science, 4446, 96-107.

10.
Kytojoki, J., Nuortio, T., Braysy, O., and Gendreau, M. (2007), An efficient variable neighborhood search heuristic for very large scale vehicle routing problems, Computers and Operations Research, 34(9), 2743-2757. crossref(new window)

11.
Mester, D., Braysy, O., and Dullaert, W. (2007), A multi-parametric evolution strategies algorithm for vehicle routing problems, Expert Systems with Applications, 32(2), 508-517. crossref(new window)

12.
METI and MLIT (2006), Guideline for Calculation Method of CO2 Emission in Logistics, Ministry of Economy, Trade and Industry, Tokyo, Japan.

13.
Mota, E., Campos, V., and Corberan, A. (2007), A new metaheuristic for the vehicle routing problem with split demands, Evolutionary Computation in Combinatorial Optimization, Lecture Notes in Computer Science, 4446, 121-129.

14.
Murata, T. and Itai, R. (2005), Multi-objective vehicle routing problems using two-fold EMO algorithms to enhance solution similarity on non-dominated solutions, Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science, 3410, 885-896.

15.
Pasia, J. M., Doerner, K. F., Hartl, R. F., and Reimann, M. (2007), A population-based local search for solving a bi-objective vehicle routing problem, Evolutionary Computation in Combinatorial Optimization, Lecture Notes in Computer Science, 4446, 166-175.

16.
Prins, C. (2004), A simple and effective evolutionary algorithm for the vehicle routing problem, Computers and Operations Research, 31(12), 1985-2002. crossref(new window)

17.
Prins, C., Prodhon, C., and Calvo, R. W. (2006), A memetic algorithm with population management (MA| PM) for the capacitated location-routing problem, Evolutionary Computation in Combinatorial Optimization, Lecture Notes in Computer Science, 3906, 183-194.

18.
Shimizu, Y. (2011a), Advanced saving method to evaluate economic concern, Transactions of The Institute of Systems, Control and Information Engineers, 24(2), 39-41. crossref(new window)

19.
Shimizu, Y. (2011b), A Meta-heuristic approach for variants of VRP in terms of generalized saving method, Transactions of the Institute of Systems, Control and Information Engineers, 24(12), 287-295. crossref(new window)

20.
Shimizu, Y. (2012), Generalized vehicle routing problem for reverse logistics aiming at low carbon transportation, Proceedings of the 13th Asia Pacific Industrial Engineering and Management Systems Conference, Puket, Thailand.

21.
Shimizu, Y. and Wada, T. (2004), Hybrid tabu search approach for hierarchical logistics optimization, Transactions of the Institute of Systems, Control and Information Engineers, 17(6), 241-248. crossref(new window)

22.
Tan, K. C., Chew, Y. H., and Lee, L. H. (2006), A hybrid multi-objective evolutionary algorithm for solving truck and trailer vehicle routing problems, European Journal of Operational Research, 172(3), 855-885. crossref(new window)

23.
Tuzun, D. and Burke, L. I. (1999), A two-phase tabu search approach to the location routing problem, European Journal of Operational Research, 116(1), 87-99. crossref(new window)

24.
Wada T. and Shimizu, Y. (2006), A hybrid meta-heuristic approach for optimal design of total supply chain network, Transactions of the Institute of Systems, Control and Information Engineers, 19(2), 69-77. crossref(new window)

25.
Watanabe, D. (2010), Analysis of location of hubterminal for air cargo in terms of generalized weber model, Communications of the Operations Research Society of Japan, 55(11), 687-692.

26.
Wu, T. H., Low, C., and Bai, J. W. (2002), Heuristic solutions to multi-depot location-routing problems, Computers and Operations Research, 29(10), 1393-1415. crossref(new window)

27.
Zhao, Q. H., Chen, S. and Zang, C. X. (2008), Model and algorithm for inventory/routing decision in a threeechelon logistics system, European Journal of Operational Research, 191(3), 623-635. crossref(new window)

28.
Zhong, Y. and Cole, M. H. (2005), A vehicle routing problem with backhauls and time windows: a guided local search solution, Transportation Research Part E: Logistics and Transportation Review, 41(2), 131-144. crossref(new window)