Particle Swarm Optimization in Solving Vehicle Routing Problem

The Vehicle Routing Problem (VRP) is a NP complete problem and is also a hot topic in the operational research. But traditional methods might suffer from slow convergence and the curse of large sizes, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO) is known to effectively solve engineering optimization problems. In this paper, the PSO in solving VRP is comprehensive surveyed from two basic aspects: the improved PSO algorithms and the particle encoding method. For each application, technical details that are required are all discussed. Finally, a summary is given together with suggestions for future research.

[1]  Huang De-xian.  Hybrid particle swarm optimization for vehicle routing problem with multiple objectives , 2007 .

[2]  Shi Zhong-ke.  Hybrid Particle Swarm Algorithm for Vehicle Routing Problem in Road Networks , 2006 .

[3]  Sha-sha Wang,et al.  A Hybrid Particle Swarm Optimization Algorithm for Vehicle Routing Problem with Stochastic Travel Time , 2008, ACFIE.

[4]  Tan Qing-mei.  Hybrid particle swarm optimization algorithm for stochastic vehicle routing problem , 2006 .

[5]  Wu Yao-hua.  Particle Swarm Optimization with near neighborhood factor based on Vehicle Routing Problem , 2008 .

[6]  Wei Ming.  Selfadaptive Chaos Particle Swarm Optimization for Allied Vehicle Routing Problems , 2008 .

[7]  George B. Dantzig,et al.  The Truck Dispatching Problem , 1959 .

[8]  Stefan Voß,et al.  Meta-heuristics: The State of the Art , 2000, Local Search for Planning and Scheduling.

[9]  Wu Yong.  Research on parallel particle swarm optimization algorithm for vehicle routing problem , 2007 .

[10]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[11]  Zhang Zhixia,et al.  Application of the improved particle swarm optimizer to vehicle routing and scheduling problems , 2007, 2007 IEEE International Conference on Grey Systems and Intelligent Services.

[12]  Wang Ya-jun.  Hybrid Algorithm on Multi-Depots Vehicle Routing Problem , 2007 .

[13]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[14]  Voratas Kachitvichyanukul.  A Study on Adaptive Particle Swarm Optimizationfor Solving Vehicle Routing Problems , 2008 .

[15]  Xia Meng-yu.  Parallel particle swarm optimization algorithm for vehicle routing problems with time windows , 2007 .

[16]  Gao Cheng-xiu,et al.  Advanced Particle Swarm Optimization for the Vehicle Routing Problem with Stochastic Demands , 2007 .

[17]  Bin Wu,et al.  A Novel Real Number Encoding Method of Particle Swarm Optimization for Vehicle Routing Problem , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[18]  Voratas Kachitvichyanukul,et al.  Particle swarm optimization and two solution representations for solving the capacitated vehicle routing problem , 2009, Comput. Ind. Eng..

[19]  Shi Hong-bo.  Improved Particle Swarm Optimization for Vehicle Routing Problem with Non-Full Load , 2006 .

[20]  Shiyou Yang,et al.  A particle swarm optimization-based method for multiobjective design optimizations , 2005, IEEE Transactions on Magnetics.

[21]  Xiaohui Hu,et al.  Engineering optimization with particle swarm , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[22]  Lu Xiaohong,et al.  Research on scheduling problem in lean production for crane service system based on queue theory , 2007 .

[23]  Liang Shuo.  A Hybrid Particle Swarm Optimization Algorithm for Vehicle Routing Problem with Time Windows , 2006 .

[24]  Ling Wang,et al.  An effective co-evolutionary particle swarm optimization for constrained engineering design problems , 2007, Eng. Appl. Artif. Intell..

[25]  Ji Yi-mu.  Application of Improved Particle Swarm Optimization in VRP , 2008 .

[26]  Voratas Kachitvichyanukul,et al.  A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery , 2009, Comput. Oper. Res..