Innovative Distributed Maintenance Concept From the Design to Cost Optimisation
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Published
Jul 26, 2025
Maria Di Mascolo
ZINEB SIMEU ABAZI
Rony DJEUNANG
Abstract
This study proposes an integrated heuristic framework for the strategic optimization of distributed maintenance operations in geo-distributed production systems (GDPS). It introduces a dual-entity maintenance structure comprising a Centralized Maintenance Workshop (CMW) and a Mobile Maintenance Workshop (MMW), aimed at minimizing total long-term maintenance costs. The cost function incorporates transport, operations, and downtime penalties, optimized via a two-stage algorithmic approach: a Maintenance Planning Algorithm (MPA) based on predictive maintenance scheduling, and a Long-term Heuristic Scheduling Algorithm (LHSA) addressing a capacitated vehicle routing problem with time windows (CVRPTW). A novel contribution includes a heuristic for CMW location determination using the weighted barycentre of site failure probabilities and a discrete selection of MMW capacities. Mixed Integer Linear Programming (MILP) and a divide-and-conquer heuristic are utilized to handle the NP-hard nature of the problem. Experimental validation using Weibull-distributed failure data and various cost scenarios demonstrates that the proposed Optimised Maintenance and Capacitated Routing (OMCR) framework can reduce lifecycle maintenance costs by up to 50%, with increased scalability for systems exceeding 30 GDPS. The framework is applicable to sectors requiring high availability and centralized servicing, including aerospace, railway, and energy industries.
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Keywords
centralised maintenance workshop, predictive maintenance, long-term scheduling, vehicle routing problem
References
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Camci, F., 2015. Maintenance scheduling of geographically distributed assets with prognostics information. Eur. J. Oper. Res. https://doi.org/10.1016/j.ejor.2015.03.023
Djeunang Mezafack, R.A., Di Mascolo, M., Simeu-Abazi, Z., 2022. Systematic literature review of repair shops: focus on sustainability. Int. J. Prod. Res. 60, 7093–7112. https://doi.org/10.1080/00207543.2021.2002965
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Fontecha, J.E., Guaje, O.O., Duque, D., Akhavan-Tabatabaei, R., Rodriguez, J.P., Medaglia, A.L., 2020. Combined maintenance and routing optimization for large-scale sewage cleaning. Ann. Oper. Res. https://doi.org/10.1007/s10479-019-03342-8
Gopalakrishnan, M., Subramaniyan, M., Skoogh, A., 2022. Data-driven machine criticality assessment – maintenance decision support for increased productivity. Prod. Plan. Control 33, 1–19. https://doi.org/10.1080/09537287.2020.1817601
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Hani, Y., Amodeo, L., Yalaoui, F., Chen, H., 2007. Ant colony optimization for solving an industrial layout problem. Eur. J. Oper. Res. 183, 633–642. https://doi.org/10.1016/j.ejor.2006.10.032
Hedjazi, D., Layachi, F., Boubiche, D.E., 2019. A multi-agent system for distributed maintenance scheduling. Comput. Electr. Eng. 77, 1–11. https://doi.org/10.1016/j.compeleceng.2019.04.016
Jia, C., Zhang, C., 2020. Joint optimization of maintenance planning and workforce routing for a geographically distributed networked infrastructure. IISE Trans. https://doi.org/10.1080/24725854.2019.1647478
Konstantakopoulos, G.D., Gayialis, S.P., Kechagias, E.P., 2020. Vehicle routing problem and related algorithms for logistics distribution: a literature review and classification. Oper. Res. https://doi.org/10.1007/s12351-020-00600-7
López-Santana, E., Akhavan-Tabatabaei, R., Dieulle, L., Labadie, N., Medaglia, A.L., 2016. On the combined maintenance and routing optimization problem. Reliab. Eng. Syst. Saf. 145, 199–214. https://doi.org/10.1016/j.ress.2015.09.016
Manco, P., Rinaldi, M., Caterino, M., Fera, M., Macchiaroli, R., 2022. Maintenance management for geographically distributed assets: a criticality-based approach. Reliab. Eng. Syst. Saf. 218, 108148. https://doi.org/10.1016/j.ress.2021.108148
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Sanchez, D.T., Boyacı, B., Zografos, K.G., 2020. An optimisation framework for airline fleet maintenance scheduling with tail assignment considerations. Transp. Res. Part B Methodol. 133, 142–164. https://doi.org/10.1016/j.trb.2019.12.008
Sedghi, M., Kauppila, O., Bergquist, B., Vanhatalo, E., Kulahci, M., 2021. A taxonomy of railway track maintenance planning and scheduling: A review and research trends. Reliab. Eng. Syst. Saf. 215, 107827. https://doi.org/10.1016/j.ress.2021.107827
Si, G., Xia, T., Pan, E., Xi, L., 2022. Service-oriented global optimization integrating maintenance grouping and technician routing for multi-location multi-unit production systems. IISE Trans. 54, 894–907. https://doi.org/10.1080/24725854.2021.1957181
Simeu-Abazi, Z., Ahmad, A.A., 2011. Optimisation of distributed maintenance: Modelling and application to the multi-factory production. Reliab. Eng. Syst. Saf. 96, 1564–1575. https://doi.org/10.1016/j.ress.2011.05.011
Simeu-Abazi, Z., Gascard, E., 2020. Implementation of a cost optimization algorithm in a context of distributed maintenance, in: 2020 International Conference on Control, Automation and Diagnosis (ICCAD). Presented at the 2020 International Conference on Control, Automation and Diagnosis (ICCAD), pp. 1–6. https://doi.org/10.1109/ICCAD49821.2020.9260507
Sleptchenko, A., Turan, H.H., Pokharel, S., ElMekkawy, T.Y., 2019. Cross-training policies for repair shops with spare part inventories. Int. J. Prod. Econ. 209, 334–345. https://doi.org/10.1016/j.ijpe.2017.12.018
Tang, H., Miller-Hooks, E., Tomastik, R., 2007. Scheduling technicians for planned maintenance of geographically distributed equipment. Transp. Res. Part E Logist. Transp. Rev. 43, 591–609. https://doi.org/10.1016/j.tre.2006.03.004
Valet, A., Altenmüller, T., Waschneck, B., May, M.C., Kuhnle, A., Lanza, G., 2022. Opportunistic maintenance scheduling with deep reinforcement learning. J. Manuf. Syst. 64, 518–534. https://doi.org/10.1016/j.jmsy.2022.07.016
Wang, K., Djurdjanovic, D., 2018. Joint Optimization of Preventive Maintenance, Spare Parts Inventory and Transportation Options for Systems of Geographically Distributed Assets. MACHINES. https://doi.org/10.3390/machines6040055
Wu, S., Coolen, F.P.A., 2013. A cost-based importance measure for system components: An extension of the Birnbaum importance. Eur. J. Oper. Res. 225, 189–195. https://doi.org/10.1016/j.ejor.2012.09.034
Yulong, L., Chi, Z., Chuanzhou, J., Xiaodong, L., Yimin, Z., 2019. Joint optimization of workforce scheduling and routing for restoring a disrupted critical infrastructure. Reliab. Eng. Syst. Saf. https://doi.org/10.1016/j.ress.2019.106551
Zhang, C., Yang, T., 2021. Optimal maintenance planning and resource allocation for wind farms based on non-dominated sorting genetic algorithm-ΙΙ. Renew. Energy 164, 1540–1549. https://doi.org/10.1016/j.renene.2020.10.125
Borcinova, Z., 2017. Two models of the capacitated vehicle routing problem. Croat. Oper. Res. Rev. 8, 463–469. https://doi.org/10.17535/crorr.2017.0029
Cakirgil, S., Yucel, E., Kuyzu, G., 2020. An integrated solution approach for multi-objective, multi-skill workforce scheduling and routing problems. Comput. Oper. Res. https://doi.org/10.1016/j.cor.2020.104908
Camci, F., 2015. Maintenance scheduling of geographically distributed assets with prognostics information. Eur. J. Oper. Res. https://doi.org/10.1016/j.ejor.2015.03.023
Djeunang Mezafack, R.A., Di Mascolo, M., Simeu-Abazi, Z., 2022. Systematic literature review of repair shops: focus on sustainability. Int. J. Prod. Res. 60, 7093–7112. https://doi.org/10.1080/00207543.2021.2002965
Drake, J.H., Starkey, A., Owusu, G., Burke, E.K., 2020. Multiobjective evolutionary algorithms for strategic deployment of resources in operational units. Eur. J. Oper. Res. 282, 729–740. https://doi.org/10.1016/j.ejor.2019.02.002
Duteil, M., 2016. 15 TGV pour Alstom : une commande au prix fort pour l'État [WWW Document], URL https://www.europe1.fr/economie/15-tgv-pour-alstom-une-commande-au-prix-fort-pour-letat-2864806 (accessed 6.19.23).
Fontecha, J.E., Guaje, O.O., Duque, D., Akhavan-Tabatabaei, R., Rodriguez, J.P., Medaglia, A.L., 2020. Combined maintenance and routing optimization for large-scale sewage cleaning. Ann. Oper. Res. https://doi.org/10.1007/s10479-019-03342-8
Gopalakrishnan, M., Subramaniyan, M., Skoogh, A., 2022. Data-driven machine criticality assessment – maintenance decision support for increased productivity. Prod. Plan. Control 33, 1–19. https://doi.org/10.1080/09537287.2020.1817601
Gupta, D., 2003. A new algorithm to solve Vehicle Routing Problems (VRPs). Int. J. Comput. Math. https://doi.org/10.1080/0020716022000005537
Hani, Y., Amodeo, L., Yalaoui, F., Chen, H., 2007. Ant colony optimization for solving an industrial layout problem. Eur. J. Oper. Res. 183, 633–642. https://doi.org/10.1016/j.ejor.2006.10.032
Hedjazi, D., Layachi, F., Boubiche, D.E., 2019. A multi-agent system for distributed maintenance scheduling. Comput. Electr. Eng. 77, 1–11. https://doi.org/10.1016/j.compeleceng.2019.04.016
Jia, C., Zhang, C., 2020. Joint optimization of maintenance planning and workforce routing for a geographically distributed networked infrastructure. IISE Trans. https://doi.org/10.1080/24725854.2019.1647478
Konstantakopoulos, G.D., Gayialis, S.P., Kechagias, E.P., 2020. Vehicle routing problem and related algorithms for logistics distribution: a literature review and classification. Oper. Res. https://doi.org/10.1007/s12351-020-00600-7
López-Santana, E., Akhavan-Tabatabaei, R., Dieulle, L., Labadie, N., Medaglia, A.L., 2016. On the combined maintenance and routing optimization problem. Reliab. Eng. Syst. Saf. 145, 199–214. https://doi.org/10.1016/j.ress.2015.09.016
Manco, P., Rinaldi, M., Caterino, M., Fera, M., Macchiaroli, R., 2022. Maintenance management for geographically distributed assets: a criticality-based approach. Reliab. Eng. Syst. Saf. 218, 108148. https://doi.org/10.1016/j.ress.2021.108148
Mariescu-Istodor, R., Cristian, A., Negrea, M., Cao, P., 2021. VRPDiv: A Divide and Conquer Framework for Large Vehicle Routing Problems. ACM Trans. Spat. Algorithms Syst. 7, 23:1-23:41. https://doi.org/10.1145/3474832
Meng, F.C., 2000. Relationships of Fussell–Vesely and Birnbaum importance to structural importance in coherent systems. Reliab. Eng. Syst. Saf. 67, 55–60. https://doi.org/10.1016/S0951-8320(99)00043-5
Nguyen, H.S.H., Do Van, P., Vu, H.C., Iung, B., 2019. Dynamic maintenance grouping and routing for geographically dispersed production systems. Reliab. Eng. Syst. Saf. 185, 392–404. https://doi.org/10.1016/j.ress.2018.12.031
Rashidnejad, M., Ebrahimnejad, S., Safari, J., 2018. A bi-objective model of preventive maintenance planning in distributed systems considering vehicle routing problem. Comput. Ind. Eng. 120. https://doi.org/10.1016/j.cie.2018.05.001
Razavi Al-e-hashem, S.A., Papi, A., Pishvaee, M.S., Rasouli, M., 2022. Robust maintenance planning and scheduling for multi-factory production networks considering disruption cost: a bi-objective optimization model and a metaheuristic solution method. Oper. Res. 22, 4999–5034. https://doi.org/10.1007/s12351-022-00733-x
Reuters, 2019. Airbus devra baisser le prix de l'A380 pour en vendre plus [WWW Document], Les Echos Investir. URL https://investir.lesechos.fr/actu-des-valeurs/la-vie-des-actions/airbus-devra-baisser-le-prix-de-la380-pour-en-vendre-plus-iag-1825402 (accessed 6.19.23).
Saihi, A., Ben-Daya, M., As’ad, R.A., 2022. Maintenance and sustainability: a systematic review of modeling-based literature. J. Qual. Maint. Eng. ahead-of-print. https://doi.org/10.1108/JQME-07-2021-0058
Sanchez, D.T., Boyacı, B., Zografos, K.G., 2020. An optimisation framework for airline fleet maintenance scheduling with tail assignment considerations. Transp. Res. Part B Methodol. 133, 142–164. https://doi.org/10.1016/j.trb.2019.12.008
Sedghi, M., Kauppila, O., Bergquist, B., Vanhatalo, E., Kulahci, M., 2021. A taxonomy of railway track maintenance planning and scheduling: A review and research trends. Reliab. Eng. Syst. Saf. 215, 107827. https://doi.org/10.1016/j.ress.2021.107827
Si, G., Xia, T., Pan, E., Xi, L., 2022. Service-oriented global optimization integrating maintenance grouping and technician routing for multi-location multi-unit production systems. IISE Trans. 54, 894–907. https://doi.org/10.1080/24725854.2021.1957181
Simeu-Abazi, Z., Ahmad, A.A., 2011. Optimisation of distributed maintenance: Modelling and application to the multi-factory production. Reliab. Eng. Syst. Saf. 96, 1564–1575. https://doi.org/10.1016/j.ress.2011.05.011
Simeu-Abazi, Z., Gascard, E., 2020. Implementation of a cost optimization algorithm in a context of distributed maintenance, in: 2020 International Conference on Control, Automation and Diagnosis (ICCAD). Presented at the 2020 International Conference on Control, Automation and Diagnosis (ICCAD), pp. 1–6. https://doi.org/10.1109/ICCAD49821.2020.9260507
Sleptchenko, A., Turan, H.H., Pokharel, S., ElMekkawy, T.Y., 2019. Cross-training policies for repair shops with spare part inventories. Int. J. Prod. Econ. 209, 334–345. https://doi.org/10.1016/j.ijpe.2017.12.018
Tang, H., Miller-Hooks, E., Tomastik, R., 2007. Scheduling technicians for planned maintenance of geographically distributed equipment. Transp. Res. Part E Logist. Transp. Rev. 43, 591–609. https://doi.org/10.1016/j.tre.2006.03.004
Valet, A., Altenmüller, T., Waschneck, B., May, M.C., Kuhnle, A., Lanza, G., 2022. Opportunistic maintenance scheduling with deep reinforcement learning. J. Manuf. Syst. 64, 518–534. https://doi.org/10.1016/j.jmsy.2022.07.016
Wang, K., Djurdjanovic, D., 2018. Joint Optimization of Preventive Maintenance, Spare Parts Inventory and Transportation Options for Systems of Geographically Distributed Assets. MACHINES. https://doi.org/10.3390/machines6040055
Wu, S., Coolen, F.P.A., 2013. A cost-based importance measure for system components: An extension of the Birnbaum importance. Eur. J. Oper. Res. 225, 189–195. https://doi.org/10.1016/j.ejor.2012.09.034
Yulong, L., Chi, Z., Chuanzhou, J., Xiaodong, L., Yimin, Z., 2019. Joint optimization of workforce scheduling and routing for restoring a disrupted critical infrastructure. Reliab. Eng. Syst. Saf. https://doi.org/10.1016/j.ress.2019.106551
Zhang, C., Yang, T., 2021. Optimal maintenance planning and resource allocation for wind farms based on non-dominated sorting genetic algorithm-ΙΙ. Renew. Energy 164, 1540–1549. https://doi.org/10.1016/j.renene.2020.10.125
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