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Annals of Operations Research

, Volume 242, Issue 2, pp 303–320 | Cite as

An improved particle swarm optimization for carton heterogeneous vehicle routing problem with a collection depot

  • Baozhen Yao
  • Bin YuEmail author
  • Ping Hu
  • Junjie Gao
  • Mingheng ZhangEmail author
Article

Abstract

In this paper, a carton heterogeneous vehicle routing problem with a collection depot is presented, which can collaboratively pick the cartons from several carton factories to a collection depot and then from the depot to serve their corresponding customers by using of heterogeneous fleet. Since the carton heterogeneous vehicle routing problem with a collection depot is a very complex problem, particle swarm optimization (PSO) is used to solve the problem in this paper. To improve the performance of the PSO, a self-adaptive inertia weight and a local search strategy are used. At last, the model and the algorithm are illustrated with two test examples. The results show that the proposed PSO is an effective method to solve the multi-depot vehicle routing problem, and the carton heterogeneous vehicle routing problem with a collection depot. Moreover, the proposed model is feasible with a saving of about 28 % in total delivery cost and could obviously reduce the required number of vehicles when comparing to the actual instance.

Keywords

Carton Heterogeneous vehicle routing problem with a collection depot Particle swarm optimization Local search Self-adaptive inertia weight 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China 51208079 and 51108053, the Trans-Century Training Program Foundation for Talents from the Ministry of Education of China NCET-12-0752, Ministry of Housing and Urban-Rural Development K520136 and the Fundamental Research Funds for the Central Universities 3013-852019.

References

  1. Ai, T. J., & Kachitvichyanukul, V. (2009). A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery. Computers & Operations Research, 36(5), 1693–1702.CrossRefGoogle Scholar
  2. Athanasopoulos, T., & Minis, L. (2013). Efficient techniques for the multi-period vehicle routing problem with time windows within a branch and price framework. Annals of Operations Research, 206(1), 1–22.CrossRefGoogle Scholar
  3. Bell, J. E., & McMullen, P. R. (2004). Ant colony optimization techniques for the vehicle routing problem. Advanced Engineering Informatics, 1(8), 41–48.CrossRefGoogle Scholar
  4. Chao, M. I., Golden, B. L., & Wasil, E. A. (1993). A new heuristic for the multi-depot vehicle routing problem that improves upon bestknown solutions. American Journal of Mathematical and Management Sciences, 13, 371–406.CrossRefGoogle Scholar
  5. Chatterjee, A., & Siarry, P. (2006). Nonlinear inertia weight variation for dynamic adaption in particle swarm optimization. Computer and Operations Research, 33(3), 859–871.CrossRefGoogle Scholar
  6. Chen, B. Y., Lam, W. H. K., Sumalee, A., & Li, Z. L. (2012). Reliable shortest path finding in stochastic networks with spatial correlated link travel times. International Journal of Geographical Information Science, 26, 365–386.CrossRefGoogle Scholar
  7. Chen, B. Y., Lam, W. H. K., Sumalee, A., Li, Q. Q., Shao, H., & Fang, Z. X. (2013a). Finding reliable shortest paths in road networks under uncertainty. Networks & Spatial Economics, 13, 123–148.CrossRefGoogle Scholar
  8. Chen, B. Y., Lam, W. H. K., Li, Q. Q., Sumalee, A., & Yan, K. (2013). Shortest path finding problem in stochastic time-dependent road networks with stochastic first-in-first-out property. IEEE Transactions on Intelligent Transportation Systems, 14(4), 1907–1917.Google Scholar
  9. Christofides, N., & Eilon, S. (1969). An algorithm for the vehicle dispatching problem. Journal of the Operational Research Society, 20, 309–318.CrossRefGoogle Scholar
  10. Cordeau, J. F., & GendreauMand Laporte, G. (1997). A tabu search heuristic for periodic and multi-depot vehicle routing problems. Networks, 30, 105–119.CrossRefGoogle Scholar
  11. Crevier, B., Cordeau, J. F., & Laporte, G. (2007). The multi-depot vehicle routing problem with inter-depot routes. European Journal of Operational Research., 176(2), 756–773.CrossRefGoogle Scholar
  12. Eberhart, R. C., & Shi, Y. H. (2001). Tracking and optimizing dynamic systems with particle swarms. Congress on Evolutionary Computation, Korea, 1, 94–100.Google Scholar
  13. Duan, Q. Y., Gupta, V. K., & Sorooshian, S. (1993). Shuffled complex evolution approach for effective and efficient minimization. Journal of optimization theory and applications, 76(3), 501–521.CrossRefGoogle Scholar
  14. Gillett, B. E., & Johnson, J. G. (1976). Multi-terminal vehicle-dispatch algorithm. Omega, 4, 711–718.CrossRefGoogle Scholar
  15. Goncalves, G. M., Gouveia, L., & Pato, M. V. (2014). An improved decomposition-based heuristic to design a water distribution network for an irrigation system. Annals of Operations Research, 219(1), 141–167.CrossRefGoogle Scholar
  16. Imran, A., Salhi, S., & Wassan, N. A. (2009). A variable neighborhood-based heuristic for the heterogeneous fleet vehicle routing problem. European Journal of Operational Research, 197(2), 509–518.CrossRefGoogle Scholar
  17. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the 1995 IEEE International Conference on Neural Networks. Perth, Aystralia, (pp. 1942–1948).Google Scholar
  18. Lin, T. L., Horng, S. J., Kao, T. W., Chen, Y. H., Run, R. S., Chen, R. J., et al. (2010). An efficient job-shop scheduling algorithm based on particle swarm optimization. Expert Systems with Applications, 37, 2629–2636.CrossRefGoogle Scholar
  19. Liu, X. F., & Chen, S. (2008). Research on profit allocation of common delivery. Service operations and logistics, and informatics. IEEE/SOLI, 2, 1505–1508.Google Scholar
  20. Mohemmed, A. W., Sahoo, N. C., & Geok, T. K. (2008). Solving shortest path problem using particle swarm optimization. Applied Soft Computing, 8(4), 1643–1653.CrossRefGoogle Scholar
  21. Mu, Q. X., & Eglese, R. W. (2013). Disrupted capacitated vehicle routing problem with order release delay. Annals of Operations Research, 207(1), 201–216.CrossRefGoogle Scholar
  22. Nickabadi, A., Ebadzadeh, M. M., & Safabakhsh, R. (2011). A novel particle swarm optimization algorithm with adaptive inertia weight. Applied Soft Computing, 11(4), 3658–3670.CrossRefGoogle Scholar
  23. Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. Computer Journal, 7(4), 308–313.CrossRefGoogle Scholar
  24. Renaud, J., Laporte, G., & Boctor, F. F. (1996). A tabu search heuristic for the multi-depot vehicle routing problem. Computers & Operations Research, 23(3), 229–235.CrossRefGoogle Scholar
  25. Shi, Y., & Eberhart, R. (2001). Fuzzy adaptive particle swarm optimization. In Congress on Evolutionary Computation Seoul, Korea.Google Scholar
  26. Yao, B. Z., Hu, P., Lu, X. H., Gao, J. J., & Zhang, M. H. (2014a). Transit network design based on travel time reliability. Transportation Research Part C, 43, 233–248.Google Scholar
  27. Yao, B. Z., Hu, P., Zhang, M. H., & Jin, M. Q. (2014b). A support vector machine with the tabu search algorithm for freeway incident detection. International Journal of Applied Mathematics and Computer Science, 24(2), 397–404.Google Scholar
  28. Yao, B. Z., Hu, P., Zhang, M. H., & Wang, S. (2013). Artificial bee colony algorithm with scanning strategy for periodic vehicle routing problem. SIMULATION: Transactions of The Society for Modeling and Simulation International, 89(6), 762–770.CrossRefGoogle Scholar
  29. Yao, Q. Z., Zhu, X. Y., & Kuo, W. (2014c). A Birnbaum-importance based genetic local search algorithm for component assignment problems. Annals of Operations Research, 212(1), 185–200.Google Scholar
  30. Yu, B., & Yang, Z. Z. (2011). An ant colony optimization model: The period vehicle routing problem with time windows. Transportation Research Part E, 47(2), 166–181.CrossRefGoogle Scholar
  31. Yu, B., Yang, Z. Z., Sun, X. S., Yao, B. Z., Zeng, Q. C., & Jeppesen, E. (2011a). Parallel genetic algorithm in bus route headway optimization. Applied Soft Computing, 11(8), 5081–5091.Google Scholar
  32. Yu, B., Yang, Z. Z., Xie, J. X. (2011b). A parallel improved ant colony optimization for multi-depot vehicle routing problem. Journal of The Operational Research Society, 62(1),183–188.Google Scholar
  33. Yu, B., Yang, Z. Z., & Yao, B. Z. (2009). An improved ant colony optimization for vehicle routing problem. European Journal Of Operational Research, 196(1), 171–176.CrossRefGoogle Scholar
  34. Yu, B., Zhu, H. B., Cai, W. J., Ma, N., & Yao, B. Z. (2013). Two-phase optimization approach to transit hub location—The case of Dalian. Journal of Transport Geography, 33, 62–71.CrossRefGoogle Scholar
  35. Yue, M., & Sun, W. (2011). Non-linear adaptive controller with a variable adaptation rate for a simulated model of an electrohydraulic actuator. Proceedings of Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering., 225(5), 603–609.Google Scholar
  36. Zachariadis, E. E., Tarantilis, C. D., & Kiranoudis, C. T. (2010). An adaptive memory methodology for the vehicle routing problem with simultaneous pick-ups and deliveries. European Journal of Operational Research, 202(2), 401–411.CrossRefGoogle Scholar
  37. Zhang, R. H., & Luo, G. R. (2008). Benefit of the common distribution based on the Shapley value. Wuhan University of Technology Journal, 30, 50–54.Google Scholar
  38. Zhang, T., Chaovalitwongse, W. A., & Zhang, Y. J. (2012). Scatter search for the stochastic travel-time vehicle routing problem with simultaneous pick-ups and deliveries. Computers & Operations Research, 39(10), 2277–2290.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.School of Automotive EngineeringDalian University of TechnologyDalianChina
  2. 2.Transportation Management CollegeDalian Maritime UniversityDalianChina

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