Particle swarm optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows
- 1.1k Downloads
Today, companies need to collect and to deliver goods from and to their depots and their customers. This problem is described as a Vehicle Routing Problem with Mixed Linehaul and Backhaul customers (VRPMB). The goods delivered from the depot to the customers can be alternated with the goods picked up. Other variants of VRP added to VRPMB are Heterogeneous fleet and Time Windows. This paper studies a complex VRP called HVRPMBTW which concerns a logistic/transport society, a problem rarely studied in literature. In this paper, we propose a Particle Swarm Optimization (PSO) with a local search. This approach has shown its effectiveness on several combinatorial problems. The adaptation of this approach to the problem studied is explained and tested on the benchmarks. The results are compared with our previous methods and they show that in several cases PSO improves the results.
KeywordsVehicle routing problem Time windows Mixed backhauls Pick up and delivery Heterogeneous fleet Particle swarm optimization
Unable to display preview. Download preview PDF.
- Ai T. J., Kachitvichyanukul V. (2007) A particle swarm optimization for the capacitated vehicle routing problem. Logistics and Supply Chain Management Systems, 2(1): 50–55Google Scholar
- Belmecheri, F., Prins, C., Yalaoui, F., & Amodeo, L. (2009a). An ant colony optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows. In 13th IFAC Symposium on INformation COntrol problems in Manufacturing (Vol. 13, pp. 1533–1538), Moscow, Russia.Google Scholar
- Belmecheri, F., Prins, C., Yalaoui, F., & Amodeo, L. (2010a). A new particle swarm optimization on vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows. In 5th International Symposium on Hydrocarbons and Chemistry-Session: Optimizations and Logistics (Layout, Transportation, Scheduling), Algiers, Algeria.Google Scholar
- Belmecheri, F., Prins, C., Yalaoui, F., & Amodeo, L. (2010b). Particle swarm optimization to solve the vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows. In 24th IEEE International Parallel and Distributed Processing Symposium, Atlanta, GA, USA.Google Scholar
- Belmecheri, F., Yalaoui, F., Prins C., & Amodeo, L. (2009b). A metaheuristic approach for solving the vehicle routing problem with heterogeneous fleet, mixed backhauls, time windows. In 40th Annual Conference of the Italian Operational Research Society, Siena, Italy.Google Scholar
- Casco, D. O., Golden, B. L., & Wasil, E. A. (1988). Vehicle routing with backhauls: Models, algorithms and case studies (pp. 127–147), North-Holland, AmsterdamGoogle Scholar
- Clerc, M. (2000). Discrete particle swarm optimization for traveling salesman problem. http://clerc.maurice.free.fr/pso/pso_tsp/Discrete_PSO_TSP.htm.
- Clerc M. (2005) L’optimisation par essaim prticulaire. Lavoisier, FranceGoogle Scholar
- Dong, G., Tang, J., Lai, K. K., & Kong, Y. (2009). An exact algorithm for vehicle routing and scheduling problem of free pickup and delivery service in flight ticket sales companies based on set-partitioning model. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-009-0311-9.
- Goldberg D. E., David E. (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Longman Publishing, Boston, MAGoogle Scholar
- Jiang, W., Zhang, Y., & Xie, J. (2009). A particle swarm optimization algorithm with crossover for vehicle routing problem with time windows. In IEEE Symposium on Computational Intelligence in Scheduling (pp. 103–106), Nashville, TN.Google Scholar
- Kennedy J., Eberhart R. C. (1995) Particle swarm optimization. Perth-Australie, Perth, pp 1942–1948Google Scholar
- Kennedy, J., & Eberhart, R. C. (1997). A discrete binary version of the particle swarm algorithm. In IEEE International Conference on Computational Cybernetics and Simulation (Vol. 5, pp. 4104–4108), Orlando, FL, USA.Google Scholar
- Kim, B. T., & Son S. J. (2010). A probability matrix based particle swarm optimization for the capacitated vehicle routing problem. Journal of Intelligent Manufacturing, (Online). doi: 10.1007/s10845-010-0455-7.
- Lin, C. T. (2008). Using predicting particle swarm optimization to solve the vehicle routing problem with time windows. In: Industrial Engineering and Engineering Management, (pp. 810–814)Google Scholar
- Machado T. R., Lopes H. S. (2005) A hybrid particle swarm optimization model for the traveling salesman problem. Springer, Vienna, pp 255–258Google Scholar
- Rieck J., Zimmermann J. (2009) A hybrid algorithm for vehicle routing of less-than-truckload carriers. Springer, Berlin Heidelberg, pp 155–171Google Scholar
- Salhi S., Nagy G. (1999) A cluster insertion heuristic for single and multiple depot vehicle routing problems with backhauling. Journal of the Operational Research Society 50(10): 1034–1042Google Scholar
- Solomon, M. M. http://w.cba.neu.edu/~msolomon/problems.htm.
- Talbi E. G. (2009) Metaheuristics: From design to implementation. Wiley, New YorkGoogle Scholar
- Vahdani, B., Tavakkoli-Moghaddam, R., Zandieh, M., & Razmi, J. (2010). Vehicle routing scheduling using an enhanced hybrid optimization approach. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-010-0427-y.
- Wang, K. P., Huang, L., Zhou, C. G., & Pang, W. (2003). Particle swarm optimization for traveling salesman problem. In International Conference on Machine Learning and Cybernetics (Vol. 3, pp. 1583–1585).Google Scholar
- Xu, Y., Wang, Q., & Hu, J. (2008). An improved discrete particle swarm optimization based on cooperative swarms. In IEEE/WIC/ACM International Conference on Intelligent Agent Technology (pp. 79–82).Google Scholar
- Yalaoui, N., Mahdi, H., Amodeo, L., & Yalaoui, F. (2009). A new approach for workshop design. doi: 10.1007/s10845-009-0368-5.
- Zhu, Q., Qian, L., Li, Y., & Zhu, S. (2006). An improved particle swarm optimization algorithm for vehicle routing problem with time windows. In IEE Congress on Evolutionary Computation (pp. 1386–1390).Google Scholar