A Hybrid Particle Swarm Optimization Algorithm for Vehicle Routing Problem with Stochastic Travel Time

Vehicle Routing Problem with stochastic travel time (VRPST) is of crucial importance in today’s industries, especially in logistics distribution. This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the problem. A chance-constraint model considering capacity of vehicle is founded. The VRPST was changed into a quasi - continuous problem by designing a real number coding. Constrained terms were processed by the penalty function. Cooperating with dynamic neighborhood and the weight value of variable inertia, the proposed HPSO can find the global optimum. The results are compared with those by both standard particle swarm optimization (SPSO) and improved genetic algorithm (IGA).The illustrations indicate that HPSO can improve success rate of searching best route and is effective for VRPST.

[1]  James P. Kelly,et al.  A Network Flow-Based Tabu Search Heuristic for the Vehicle Routing Problem , 1996, Transp. Sci..

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

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

[4]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[5]  Roger L. Wainwright,et al.  Multiple Vehicle Routing with Time and Capacity Constraints Using Genetic Algorithms , 1993, ICGA.

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

[7]  Gilbert Laporte,et al.  The Vehicle Routing Problem with Stochastic Travel Times , 1992, Transp. Sci..

[8]  Imtiaz Ahmad,et al.  Particle swarm optimization for task assignment problem , 2002, Microprocess. Microsystems.