期刊论文详细信息
Mathematics
Multi-Objective Model and Variable Neighborhood Search Algorithms for the Joint Maintenance Scheduling and Workforce Routing Problem
Cyril Fonlupt1  Rym Nesrine Guibadj1  Lamiaa Dahite2  Rachid Benmansour2  Abdeslam Kadrani2 
[1] LISIC—UR 4491, Laboratoire d’Informatique, Signal et Image de la Côte d’Opale, Université du Littoral Côte d’Opale, 62228 Calais, France;SI2M, Laboratoire Systèmes d’Information, Systèmes Intelligents et Modélisation Mathématique, Institut National de Statistique et d’Economie Appliqué, Rabat 10100, Morocco;
关键词: time-based maintenance;    vehicle routing problem;    random failures;    multi-objective optimization;    variable neighborhood descent;    general variable neighborhood search;   
DOI  :  10.3390/math10111807
来源: DOAJ
【 摘 要 】

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.

【 授权许可】

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