Authors: Hiba Yahyaoui; Saoussen Krichen; Abdelkader Dekdouk
Addresses: LARODEC, Institut Supérieur de Gestion, University of Tunis, Tunisia ' LARODEC, Institut Supérieur de Gestion, University of Tunis, Tunisia ' College of Arts and Applied Sciences, Dhofar University, Sultanate of Oman
Abstract: We address in this paper a delivery process with time requirements in the supply chain stated as follows: orders launched from customers are centralised and assigned to firms' depots for the delivery process. The consideration of a depot and a set of customers belonging to different firms are seen as a VRPTW that serves n customers using a subset of vehicles. We implement in this paper a DSS that handles the delivering activity in the supply chain. The DSS embeds a GRASP and genetic components for generating promising solution in a concurrently run time. In addition we proposed a V NSH-based hyper-heuristic that tries to select the most accurate neighbourhood at each iteration to apply according to its previous performance. Simulation results are conducted on Solomon's benchmarks. The DSS recorded very competitive results regarding state-of-the-art approaches.
Keywords: hybrid genetic algorithm; variable neighbourhood search; VRPTW; hyper-heuristic.
International Journal of Services and Operations Management, 2019 Vol.33 No.4, pp.529 - 544
Received: 18 Oct 2016
Accepted: 11 Jun 2017
Published online: 13 Aug 2019 *