Two-stage BSO-SA Algorithm for Fleet Size and Mixed Vehicle Routing Problem with Unilateral Soft Time Window
Based on the standard brainstorming algorithm(BSO),a new two-stage brainstorming an-nealing algorithm(BSO-SA)was proposed.According to the multi-vehicle problem,a coding and de-coding form based on greedy algorithm was designed.Kmeans clustering in BSO algorithm was re-placed by Kmedoids clustering to improve the clustering performance of the algorithm.Meanwhile,four local search operators were adopted to improve the efficiency of generating new solutions.The i-dea of two-stage solution solves the problems that BSO algorithm is easy to fall into local optimum and SA algorithm converges slowly.Three numerical examples with different scales are used for verifica-tion,and compared with simulated annealing,genetic algorithm and brainstorming algorithm.The re-sults show that the algorithm is effective.
vehicle routing problembrain storm optimizationtwo-stageunilateral soft time window
万方数据
维普
