Title: A decision-making system for container storage management in a seaport using the ant-colony optimisation algorithm

Authors: Fouzia Amrani; Khadidja Yachba; Naima Belayachi; Djamila Hamdadou

Addresses: LIO Laboratory, Department of Mathematical and Computer Sciences, National Polytechnic School of Oran, University of Oran 1, Ahmed Benbella, B.P. 1505 El M'Naouer, Oran, 31000, Algeria ' LIO Laboratory, Department of Computer Sciences, University Center of Relizane, BP 48000, Relizane, 48000, Algeria ' LIO Laboratory, Department of Computer Sciences, Higher National School, Oran, Algeria ' LIO Laboratory, Department of Computer Sciences, University of Oran 1, Ahmed Benbella, BP 1524, ElM'naouer, Oran, 31000, Algeria

Abstract: This article addresses the problem of container storage in a container terminal. Container terminals constitute essential intermodal interfaces for the global transport network. The optimal location for a container in a terminal is very important to port operators as it directly impacts the overall performance of the container terminal and helps maximise throughput due to the higher efficiency it provides. In our work, we propose an approach to solving the container storage problem by describing a decision model that simulates, solves and optimises available storage space to handle the departures and arrivals of full containers in a seaport. In other words, this is a model that minimises the total number of unnecessary movements while respecting the space and time dynamics of the environment. The interest of this work concerns the development of a software tool that identifies the best location for a container using the ant colony algorithm that is integrated into a decisional model. The latter is being proposed to the port operator for a possible choice.

Keywords: container terminal; container storage; optimal location; storage area; decision model; ant-colony algorithm.

DOI: 10.1504/IJMDM.2018.093501

International Journal of Management and Decision Making, 2018 Vol.17 No.3, pp.348 - 367

Available online: 28 Jun 2018 *

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