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Adaptive large neighborhood search heuristics for the vehicle routing problem with stochastic demands and weight-related cost
Institution:1. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, Jiangxi, People’s Republic of China;2. Department of Management Sciences, City University of Hong Kong, Tat Chee Ave, Kowloon Tong, Hong Kong;3. Department of Computer Science, Xiamen University, Xiamen 361005, China;4. School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore;5. International Center of Management Science and Engineering, School of Management and Engineering, Nanjing University, Nanjing 210093, People’s Republic of China;1. International Center of Management Science and Engineering, School of Management and Engineering, Nanjing University, Nanjing 210093, PR China;2. School of Management, Huazhong University of Science and Technology, No. 1037, Luoyu Road, Wuhan, PR China;3. Department of Management Sciences, City University of Hong Kong, Tat Chee Ave., Kowloon Tong, Kowloon, Hong Kong;1. Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China;2. Department of Business Administration, National Taiwan University, Taipei 10617, Taiwan, ROC;1. L’UNAM, Ecole des Mines de Nantes, IRCCyN UMR CNRS 6597, 4 Rue Alfred Kastler, 44307 Nantes Cedex 3, France;2. Department of Mathematics and Industrial Engineering and CIRRELT, Ecole Polytechnique de Montréal and CIRRELT, C.P 6079, Succursale Centre-ville, Montreal, QC H3C 3A7, Canada
Abstract:The vehicle routing problem (VRP) with stochastic demands and weight-related cost is an extension of the VRP. Although some researchers have studied the VRP with either stochastic demands or weight-related cost, the literature on this problem is quite limited. We adopt the a priori optimization to tackle this problem and propose a dynamic programming to compute the expected cost of each route. We develop the adaptive large neighborhood search heuristics equipped with several approximate methods for the problem. To evaluate our heuristics, we generate 84 test instances. Computational results demonstrate the performance of our heuristics and can serve as benchmarks for future researchers.
Keywords:Routing  Stochastic demands  Weight-related cost  A priori optimization  Adaptive large neighborhood search
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