Customer satisfaction in dynamic vehicle routing problem with time windows |
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| Affiliation: | 1. Laboratoire L.O.G.I.Q. – ISGIS, Université de Sfax, Route de Tunis, km 11 – B.P: 1164-3018, Sfax Tunisia;2. Laboratoire LMAH – ISEL, Université du Havre, 25 rue Philippe LEBON, B.P. 540-76058, Le Havre Cedex France;3. Institut des Hautes Etudes Commerciales de Sfax, University of Sfax, Route de Sidi Mansour km 10 – B.P. 1170-3018, Sfax Tunisia;1. Université d’Angers, LARIS (EA 7315), 62 avenue Notre Dame du Lac, 49000 Angers, France;2. Departamento de Ingeniería de Producción, Universidad EAFIT, Carrera 49 No. 7 Sur-50, Medellín, Colombia;3. Université François-Rabelais de Tours, CNRS, LI EA 6300, OC ERL CNRS 6305, 64 avenue Jean Portalis, 37200 Tours, France;4. Departamento de Ingeniería Industrial, Universidad de Antioquia, Calle 70 No. 52-21, Medellín, Colombia;1. School of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands;2. CIRRELT and HEC Montréal, 3000 chemin de la Côte-Sainte-Catherine, H3T 2A7, Montréal, Canada;1. Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland;2. School of Computer Science and Engineering, Nanyang Technological University, Block N4, Nanyang Avenue, Singapore 639798, Singapore |
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| Abstract: | The dynamic vehicle routing and scheduling problem is a well-known complex combinatorial optimization problem that drew significant attention over the past few years. This paper presents a novel algorithm introducing a new strategy to integrate anticipated future visit requests during plan generation, aimed at explicitly improving customer satisfaction. An evaluation of the proposed strategy is performed using a hybrid genetic algorithm previously designed for the dynamic vehicle problem with time windows that we modified to capture customer satisfaction over multiple visits. Simulations compare the value of the revisited algorithm exploiting the new strategy, clearly demonstrating its impact on customer satisfaction level. |
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| Keywords: | Genetic algorithms Customer satisfaction Dynamic vehicle routing |
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