Title: Ant colony optimisation combined with variable neighbourhood search for scheduling preventive railway maintenance activities

Authors: Safa Khalouli; Rachid Benmansour; Saïd Hanafi

Addresses: LAMIH UMR CNRS 8201, University of Valenciennes, Le Mont Houy, 59313 Valenciennes Cedex 9, France ' LAMIH UMR CNRS 8201, University of Valenciennes, Le Mont Houy, 59313 Valenciennes Cedex 9, France; Institut National de Statistique et d'Economie Appliquée, B.P.: 6217 Rabat-Instituts, Morocco ' LAMIH UMR CNRS 8201, University of Valenciennes, Le Mont Houy, 59313 Valenciennes Cedex 9, France

Abstract: Railway infrastructure maintenance is of fundamental importance in order to ensure a good service in terms of punctuality, safety and efficiently operation of trains on railway track and also for passenger comfort. Track maintenance covers a large amount of different activities such as inspections, repairs, and renewals. In this paper, we address the NP-hard problem of scheduling the preventive railway maintenance activities in order to minimise the overall cost of these activities. Given the complexity of the problem, we propose two meta-heuristics, a variable neighbourhood search (VNS), and an ant colony optimisation (ACO) based on opportunities to deal with this problem. Then, we develop a hybrid approach combining ACO with VNS. The performance of our proposed algorithms is tested by numerical experiments on a large number of randomly generated instances. Comparisons with optimal solutions are presented. The results show the effectiveness of our proposed methods.

Keywords: preventive maintenance; scheduling; variable neighbourhood search; VNS; ant colony optimisation; ACO; local search; rail transportation.

DOI: 10.1504/IJIEI.2018.091010

International Journal of Intelligent Engineering Informatics, 2018 Vol.6 No.1/2, pp.78 - 98

Received: 16 Nov 2016
Accepted: 22 May 2017

Published online: 02 Apr 2018 *

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