Multi-Objective Model and Variable Neighborhood Search Algorithms for the Joint Maintenance Scheduling and Workforce Routing Problem

Lamiaa Dahite, Abdeslam Kadrani, Rachid Benmansour, Rym Nesrine Guibadj and Cyril Fonlupt
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Lamiaa Dahite: SI2M, Laboratoire Systèmes d’Information, Systèmes Intelligents et Modélisation Mathématique, Institut National de Statistique et d’Economie Appliqué, Rabat 10100, Morocco
Abdeslam Kadrani: SI2M, Laboratoire Systèmes d’Information, Systèmes Intelligents et Modélisation Mathématique, Institut National de Statistique et d’Economie Appliqué, Rabat 10100, Morocco
Rachid Benmansour: SI2M, Laboratoire Systèmes d’Information, Systèmes Intelligents et Modélisation Mathématique, Institut National de Statistique et d’Economie Appliqué, Rabat 10100, Morocco
Rym Nesrine Guibadj: LISIC—UR 4491, Laboratoire d’Informatique, Signal et Image de la Côte d’Opale, Université du Littoral Côte d’Opale, 62228 Calais, France
Cyril Fonlupt: LISIC—UR 4491, Laboratoire d’Informatique, Signal et Image de la Côte d’Opale, Université du Littoral Côte d’Opale, 62228 Calais, France

Mathematics, 2022, vol. 10, issue 11, 1-37

Abstract: This paper addresses a problem faced by maintenance service providers: performing maintenance activities at the right time on geographically distributed machines subjected to random failures. This problem requires determining for each technician the sequence of maintenance operations to perform to minimize the total expected costs while ensuring a high level of machine availability. To date, research in this area has dealt with routing and maintenance schedules separately. This study aims to determine the optimal maintenance and routing plan simultaneously. A new bi-objective mathematical model that integrates both routing and maintenance considerations is proposed for time-based preventive maintenance. The first objective is to minimize the travel cost related to technicians’ routing. The second objective can either minimize the total preventive and corrective maintenance cost or the failure cost. New general variable neighborhood search (GVNS) and variable neighborhood descent (VND) algorithms based on the Pareto dominance concept are proposed and performed over newly generated instances. The efficiency of our approach is demonstrated through several experiments. Compared to the commercial solver and existing multi-objective VND and GVNS, these new algorithms obtain highly competitive results on both mono-objective and bi-objective variants.

Keywords: time-based maintenance; vehicle routing problem; random failures; multi-objective optimization; variable neighborhood descent; general variable neighborhood search (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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