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A cooperative parallel metaheuristic for the capacitated vehicle routing problem
Affiliation:1. Molde University College, Specialized University in Logistics, N-6411, Molde, Norway;2. School of Management, UQAM & Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation, Montréal, QC, Canada;1. Faculdade de Economia da Universidade do Porto, C/O Jorge Valente, Rua Dr. Roberto Frias, s/n, 4200-464 Porto, Portugal;2. LIAAD – INESCTEC LA, Faculdade de Economia da Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-464 Porto, Portugal;3. Department of Business Administration, Eastern Connecticut State University, 83 Windham St., Willimantic, CT 06226-2295, USA;1. Department of Industrial and Enterprise Systems Engineering, 117 Transportation Building, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA;2. Department of Mechanical Engineering, Indian Institute of Technology, Roorkee, UT 247667, India;1. University of Valencia, Department of Statistics and Operations Research, Burjassot, Valencia, Spain;2. Polytechnic University of Valencia, Department of Applied Statistics and Operations Research, and Quality, Valencia, Spain;1. Mathematical Institute SANU, Belgrade, Serbia;2. University of Seville, Seville, Spain
Abstract:This paper introduces a cooperative parallel metaheuristic for the capacitated vehicle routing problem. The proposed metaheuristic consists of multiple parallel tabu search threads that cooperate by asynchronously exchanging best-found solutions through a common solution pool. The solutions sent to the pool are clustered according to their similarities. The search history information identified from the solution clusters is applied to guide the intensification or diversification of the tabu search threads. Computational experiments on two sets of large-scale benchmark instance sets from the literature demonstrate that the suggested metaheuristic is highly competitive, providing new best solutions to ten of those well-studied instances.
Keywords:Vehicle routing  Parallel metaheuristic  Cooperative search  Solution clustering
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