A route-neighborhood-based metaheuristic for vehicle routing problem with time windows |
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| Affiliation: | 1. Department of Mathematics, Shahid Beheshti University, G.C., Tehran, Iran;2. Department of Mathematics, Faculty of Science, Lorestan University, Khorram Abad, Iran;1. Dept. de Informática y Estadística, Universidad Rey Juan Carlos, Móstoles, Spain;2. Lab-STICC, UMR6285 CNRS, Centre de Recherche, Université de Bretagne-Sud, Lorient, France;3. Dept. Estadística e Investigación Operativa, Universidad de Valencia, Burjassot, Spain;1. Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia;2. Department of Electrical & Electronics Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia;3. Department of Fundamental & Applied Sciences, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia;1. Department of Industrial Engineering, Universidad del Valle, Calle 13 # 100-00, Cali, Colombia;2. Department of Industrial Engineering, University of Arkansas, 4183 Bell Engineering Center, Fayetteville, AR 72701, United States |
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| Abstract: | ![]()
In this paper, a two-stage metaheuristic based on a new neighborhood structure is proposed to solve the vehicle routing problem with time windows (VRPTW). Our neighborhood construction focuses on the relationship between route(s) and node(s). Unlike the conventional methods for parallel route construction, we construct routes in a nested parallel manner to obtain higher solution quality. Valuable information extracted from the previous parallel construction runs is used to enhance the performance of parallel construction. In addition, when there are only a few unrouted customers left, we design a special procedure for handling them. Computational results for 60 benchmark problems are reported. The results indicate that our approach is highly competitive with all existing heuristics, and in particular very promising for solving problems of large size. |
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