Two-stage Particle Swarm Optimization Algorithm for the Time Dependent Alternative Vehicle Routing Problem

Journal of Applied & Computational Mathematics

article

Hsiao-Fan Wang¹  Yen-Yi Lee¹ 

[1] Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu

关键词: General system of regularized non-convex variational inequalities;    Uniformly t-prox regular sets;    Iterative schemes;    Convergence analysis;   

DOI  :  10.4172/2168-9679.1000170

来源: Hilaris Publisher

【 摘 要 】

This study considered a Time Dependent Alternative Vehicle Routing Problem (TDAVRP) in a multi-graph network (TDAVRP) and was formulated into a Mixed Integer Programming model. Due to its NP nature, an algorithm based on Particle Swarm Optimization (PSO) with local improvement was developed to speed up the solution procedure. By using different sets of Solomon’s benchmark problems and continuous travel time functions, the accuracy and efficiency of the two-stage PSO were evaluated. The computational results showed that the proposed algorithm is capable of deriving optimal or near optimal solutions in a short period of time when the size of the problems are small and is able to obtain feasible solutions within a reasonable time when solving the large problems which cannot be solved by ILOG CPLEX. In addition, Sensitivity Analysis was conducted to evaluate the performances of the parameters. The results indicated that the number of customers is a sensitive parameter and will influence the required number of vehicles, value of violations and percentage of alternative edges in the solution sets.

【 授权许可】

Unknown