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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 879614, 19 pages
Multiobjective Quantum Evolutionary Algorithm for the Vehicle Routing Problem with Customer Satisfaction
1College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
2College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, China
3Department of Mathematics, University of Salerno, Via Ponte Don Melillo, 84084 Fisciano, Italy
Received 20 August 2012; Accepted 10 October 2012
Academic Editor: Sheng-yong Chen
Copyright © 2012 Jingling Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- P. Toth and D. Vigo, The Vehicle Routing Problem, vol. 9 of SIAM Monographs on Discrete Mathematics and Applications, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, Pa, USA, 2002.
- G. B. Dantzig and J. H. Ramser, “The truck dispatching problem,” Management Science, vol. 6, pp. 80–91, 1959.
- W. Huang and S. Chen, “Epidemic metapopulation model with traffic routing in scale-free networks,” Journal of Statistical Mechanics, vol. 2011, no. 12, Article ID P12004, 19 pages, 2011.
- M. Li, W. Zhao, and S. Chen, “MBm-based scalings of traffic propagated in internet,” Mathematical Problems in Engineering, vol. 2011, Article ID 389803, 21 pages, 2011.
- S. Y. Chen, H. Tong, Z. Wang, S. Liu, M. Li, and B. Zhang, “Improved generalized belief propagation for vision processing,” Mathematical Problems in Engineering, vol. 2011, Article ID 416963, 12 pages, 2011.
- C. H. Jiang, S. G. Dai, and Y. H. Hu, “Hybrid genetic algorithm for capacitated vehicle routing problem,” Computer Integrated Manufacturing Systems, vol. 13, no. 10, pp. 2047–2052, 2007 (Chinese).
- C. Prins, “A simple and effective evolutionary algorithm for the vehicle routing problem,” Computers & Operations Research, vol. 31, no. 12, pp. 1985–2002, 2004.
- P. Reiter and W. J. Gutjahr, “Exact hybrid algorithms for solving a bi-objective vehicle routing problem,” Central European Journal of Operations Research, vol. 20, no. 1, pp. 19–43, 2012.
- S. P. Anbuudayasankar, K. Ganesh, S. C. Lenny Koh, and Y. Ducq, “Modified savings heuristics and genetic algorithm for bi-objective vehicle routing problem with forced backhauls,” Expert Systems with Applications, vol. 39, pp. 2296–2305, 2012.
- S. C. Hong and Y. B. Park, “Heuristic for bi-objective vehicle routing with time window constraints,” International Journal of Production Economics, vol. 62, no. 3, pp. 249–258, 1999.
- E. Zitzler and L. Thiele, “Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 4, pp. 257–271, 1999.
- A. Lim and F. Wang, “A smoothed dynamic tabu search embedded GRASP for m-VRPTW,” in Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI '04), pp. 704–708, November 2004.
- K. C. Tan, Y. H. Chew, and L. H. Lee, “A hybrid multiobjective evolutionary algorithm for solving vehicle routing problem with time windows,” Computational Optimization and Applications, vol. 34, no. 1, pp. 115–151, 2006.
- A. Garcia-Najera and J. A. Bullinaria, “An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows,” Computers & Operations Research, vol. 38, no. 1, pp. 287–300, 2011.
- P. Badeau, F. Guertin, M. Gendreau, J. Y. Potvin, and E. Taillard, “A parallel tabu search heuristic for the vehicle routing problem with time windows,” Transportation Research C, vol. 5, no. 2, pp. 109–122, 1997.
- K. C. Tan, L. H. Lee, Q. L. Zhu, and K. Ou, “Heuristic methods for vehicle routing problem with time windows,” Artificial Intelligence in Engineering, vol. 15, no. 3, pp. 281–295, 2001.
- J. Berger and M. Barkaoui, “A parallel hybrid genetic algorithm for the vehicle routing problem with time windows,” Computers & Operations Research, vol. 31, no. 12, pp. 2037–2053, 2004.
- A. Le Bouthillier and T. G. Crainic, “A cooperative parallel meta-heuristic for the vehicle routing problem with time windows,” Computers and Operations Research, vol. 32, no. 7, pp. 1685–1708, 2005.
- G. B. Alvarenga, G. R. Mateus, and G. de Tomi, “A genetic and set partitioning two-phase approach for the vehicle routing problem with time windows,” Computers and Operations Research, vol. 34, no. 6, pp. 1561–1584, 2007.
- R. Cheng and M. Gen, “Vehicle routing problem with fuzzy due-time using genetic algorithms,” Japanese Journal of Fuzzy Theory and Systems, vol. 7, no. 5, pp. 1050–1061, 1995.
- R. Cheng and M. Gen, “Fuzzy vehicle routing and scheduling problem using genetic algorithm,” in Genetic Algorithms and Soft Computing, F. Herrera and J. Verdegay, Eds., pp. 683–709, Springer, 1996.
- S. Wen, W. Zheng, J. Zhu, X. Li, and S. Chen, “Elman fuzzy adaptive control for obstacle avoidance of mobile robots using hybrid force/position incorporation,” IEEE Transactions on Systems, Man and Cybernetics C, vol. 42, no. 4, pp. 603–608, 2012.
- S. Y. Chen, J. Zhang, H. Zhang, N. M. Kwok, and Y. F. Li, “Intelligent lighting control for vision-based robotic manipulation,” IEEE Transactions on Industrial Electronics, vol. 59, no. 8, pp. 3254–33263, 2012.
- C. Cattani, “Shannon wavelets for the solution of integrodifferential equations,” Mathematical Problems in Engineering, vol. 2010, Article ID 408418, 22 pages, 2010.
- J. Y. Zhang, Y. H. Guo, and J. Li, “Research of multi-objective fuzzy vehicle scheduling problem based on satisfaction of customers,” Journal of the China Railway Society, vol. 25, no. 2, pp. 15–17, 2003 (Chinese).
- Y. J. Jia, Optimal algorithm research of vehicle scheduling problem [Ph.D. thesis], Shanghai Jiaotong University, 2004.
- B. Wu, Particle swarm optimization for velaicle routing problem and its application [Ph.D. thesis], Zhejiang University of Technology, 2008.
- J.-J. Lin, “A GA-based multi-objective decision making for optimal vehicle transportation,” Journal of Information Science and Engineering, vol. 24, no. 1, pp. 237–260, 2008.
- C. H. Wang and C. H. Li, “Optimization of an established multi-objective delivering problem by an improved hybrid algorithm,” Expert Systems with Applications, vol. 38, no. 4, pp. 4361–4367, 2011.
- K.-H. Han and J.-H. Kim, “Quantum-inspired evolutionary algorithm for a class of combinatorial optimization,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 6, pp. 580–593, 2002.
- M. Fonseca Carlos and J. Peter Fleming, “Genetic algorithm for multiobjecetive optimization: formulation, dicussion and generalization,” in Proceeding of the 5th International Conference on Genetic Algorithm, pp. 416–423, 1993.
- S. Chen, Y. Zheng, C. Cattani, and W. Wang, “Modeling of biological intelligence for SCM system optimization,” Computational and Mathematical Methods in Medicine, Article ID 769702, 10 pages, 2012.
- S. Y. Chen and Y. F. Li, “Automatic Sensor Placement for Model-Based Robot Vision,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 34, no. 1, pp. 393–408, 2004.
- Z. Jinhua, Multi-Objective Evolutionary Algorithm and Its Application, Science Press, Beijing, China, 2007.
- J. Knowles and D. W. Corne, “The pareto archived evolution strategy: a new baseline algorithm foe pareto multiobjective optimisation,” in Proceeding of the Congress of Evolutionary Computation, pp. 98–105, 1999.
- D. W. Corne, D. K. Joshua, and J. O. Martin, “The pareto envelope-based selection algorithm for multiobjective optimization,” in Proceeding of the Parallel Problem Solving from Nature VI Conference, pp. 839–848, Springer, 2000.
- A. Narayanan and M. Moore, “Quantum-inspired genetic algorithms,” in Proceedings of IEEE International Conference on Evolutionary Computation (ICEC '96), pp. 61–66, May 1996.
- J. C. Bean, “Genetic alogrithms and random keys for sequedcing and optimization,” ORSA Journal on Computing, vol. 6, pp. 154–160, 1994.
- Z. Jingling, Z. Yanwei, et al., “A hybrid quantum-inspired evolutionary algorithm for capacitated vehicle routing problem,” in Proceedings Advanced Intelligent Computing Theories and Applications, vol. 5226, pp. 31–38, Springer, Berlin, Germany, 2008.