Skewed general variable neighborhood search for the location routing scheduling problem |
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| Affiliation: | 1. Institut de Recherche Technologique Railenium, F-59300 Famars, France;2. LAMIH-UMR CNRS 8201, Université de Valenciennes et du Hainaut-Cambrésis, Le Mont Houy, 59313 Valenciennes Cedex 9, France;3. Centro de Investigação Algoritmi, Escola de Engenharia, Universidade do Minho , 4710-057, Braga, Portugal;4. MODILS, Université de Sfax, Faculté de sciences économiques et de gestion, route de l''aéroport km 4, Sfax 3018, Tunisia;1. Department of Computer Science and Information Engineering, Ming Chuan University, Taoyuan, Taiwan;2. Department of Electronic Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan;1. School of Mathematical Sciences, Heilongjiang University, Harbin 150080, China;2. School of Applied Sciences, Harbin University of Science and Technology, Harbin 150080, China;3. School of Mathematical Sciences, Harbin Normal University, Harbin 150025, China;1. CMUC, Departamento de Matemática, Universidade de Coimbra, Apartado 3008, 3001-454 Coimbra, Portugal;2. Departamento de Matemáticas, Universidad de Cádiz, Campus de Puerto Real, 11510, Puerto Real, Cádiz, Spain |
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| Abstract: | The integrated location routing scheduling problem is a variant of the well-known location routing problem. The location routing problem consists in selecting a set of depots to open and in building a set of routes from these depots, to serve a set of customers at minimum cost. In this variant, a vehicle can perform more than a single route in the planning period. As a consequence, the routes have to be scheduled within the workdays of each vehicle. The problem arises typically when routes are constrained to have a short duration. It happens for example within the boundaries of small geographic areas or in the transportation of perishable goods. In this paper, we propose a skewed general variable neighborhood search based heuristic to solve it. The algorithm is tested extensively and we show that it is efficient and provides the proven optimal solution in a significant number of cases. Moreover, it clearly outperforms a multi-start VND based heuristic that uses the same neighborhood structures. |
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| Keywords: | Location Routing Scheduling Variable neighborhood search |
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