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Outsourcing optimization in two-echelon supply chain network under integrated production-maintenance constraints

Author

Listed:
  • Mohammed Haoues

    (Batna 2 University)

  • Mohammed Dahane

    (Université de Lorraine
    Production and Maintenance)

  • Nadia Kenza Mouss

    (Batna 2 University)

Abstract

In this paper, we study a two-echelon supply chain network consisting of multi-outsourcers and multi-subcontractors. Each one is composed of a failure-prone production unit that produces a single product to fulfil market demands with variable production rates. Sometimes the manufacturing systems are not able to satisfy demand; in this case, outsourcing option is adopted to improve the limited in-house production capacity. The outsourcing is not justified by the production lack of manufacturing systems, but is also considered for the costs minimization issues. In the considered problem, we assume that the failure rate is dependent on the time and production rate. Preventive maintenance activities can be conducted to mitigate the deterioration effects, and minimal repairs are performed when unplanned failures occurs. We consider that the production cost depends on the rate of the machine utilization. The aim of this research is to propose a joint policy based on a mixed integer programming formulation to balance the trade-off between two-echelon of supply chain. We seek to assist outsourcers to determine the integrated in-house/ outsourcing, and maintenance plans, and the subcontractors to determine the integrated production-maintenance plans so that the benefit of the supply chain is maximized over a finite planning horizon. We develop an improved optimization procedure based on the genetic algorithms, and we discuss and conduct computational experiments to study the managerial insights for the developed framework.

Suggested Citation

  • Mohammed Haoues & Mohammed Dahane & Nadia Kenza Mouss, 2019. "Outsourcing optimization in two-echelon supply chain network under integrated production-maintenance constraints," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 701-725, February.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:2:d:10.1007_s10845-016-1273-3
    DOI: 10.1007/s10845-016-1273-3
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    References listed on IDEAS

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    Cited by:

    1. Muhammad Ameer & Mohammed Dahane, 2023. "Reconfigurability improvement in Industry 4.0: a hybrid genetic algorithm-based heuristic approach for a co-generation of setup and process plans in a reconfigurable environment," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1445-1467, March.
    2. A. Khatab & C. Diallo & E.-H. Aghezzaf & U. Venkatadri, 2022. "Optimization of the integrated fleet-level imperfect selective maintenance and repairpersons assignment problem," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 703-718, March.

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