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

Author

Listed:
  • Lamiaa Dahite

    (SI2M, Laboratoire Systèmes d’Information, Systèmes Intelligents et Modélisation Mathématique, Institut National de Statistique et d’Economie Appliqué, Rabat 10100, Morocco
    LISIC—UR 4491, Laboratoire d’Informatique, Signal et Image de la Côte d’Opale, Université du Littoral Côte d’Opale, 62228 Calais, France)

  • 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)

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.

Suggested Citation

  • Lamiaa Dahite & Abdeslam Kadrani & Rachid Benmansour & Rym Nesrine Guibadj & Cyril Fonlupt, 2022. "Multi-Objective Model and Variable Neighborhood Search Algorithms for the Joint Maintenance Scheduling and Workforce Routing Problem," Mathematics, MDPI, vol. 10(11), pages 1-37, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:11:p:1807-:d:823640
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    References listed on IDEAS

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    1. Abraham Duarte & Juan Pantrigo & Eduardo Pardo & Nenad Mladenovic, 2015. "Multi-objective variable neighborhood search: an application to combinatorial optimization problems," Journal of Global Optimization, Springer, vol. 63(3), pages 515-536, November.
    2. Dubois-Lacoste, Jérémie & López-Ibáñez, Manuel & Stützle, Thomas, 2015. "Anytime Pareto local search," European Journal of Operational Research, Elsevier, vol. 243(2), pages 369-385.
    3. López-Santana, Eduyn & Akhavan-Tabatabaei, Raha & Dieulle, Laurence & Labadie, Nacima & Medaglia, Andrés L., 2016. "On the combined maintenance and routing optimization problem," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 199-214.
    4. Jbili, S. & Chelbi, A. & Radhoui, M. & Kessentini, M., 2018. "Integrated strategy of Vehicle Routing and Maintenance," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 202-214.
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