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Evaluation of the Effects of a Machine Failure on the Robustness of a Job Shop System—Proactive Approaches

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  • Iwona Paprocka

    (Faculty of Mechanical Engineering, Silesian University of Technology, Konarskiego 18A str., 44-100 Gliwice, Poland)

Abstract

Researchers are searching for opportunities to organize production systems that save energy and natural resources. Preventive maintenance (PM) is essential for the efficient use of machines and energy saving. Any rework due to a machine failure consumes additional energy, human resources, equipment, spare parts and raw materials. Two criteria—quality robustness (QR) and solution robustness (SR)—are used in order to compute the operational efficiency of the production system in the event of disruption. Any cost criterion can be added to the QR in order to measure losses due to a machine failure. The SR criterion measures a number of changes necessary to adopt the production schedule after the machine failure. Two proactive approaches are compared to compute the operational efficiency. In the predictive-reactive approach, the PM time is predicted and a stable schedule is built. In the proactive-reactive approach, a schedule is achieved for the best sequence of idle times between jobs. The influence of disturbance on both schedules using robustness measures is examined. This paper presents the results of computer simulations for the above approaches. The approaches are compared in order to select a better method of production organization that reduces costs and waste due to machine failure.

Suggested Citation

  • Iwona Paprocka, 2018. "Evaluation of the Effects of a Machine Failure on the Robustness of a Job Shop System—Proactive Approaches," Sustainability, MDPI, vol. 11(1), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:11:y:2018:i:1:p:65-:d:192668
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    References listed on IDEAS

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    1. Xia, Tangbin & Jin, Xiaoning & Xi, Lifeng & Ni, Jun, 2015. "Production-driven opportunistic maintenance for batch production based on MAM–APB scheduling," European Journal of Operational Research, Elsevier, vol. 240(3), pages 781-790.
    2. Iwona Paprocka & Bożena Skołud, 2017. "A hybrid multi-objective immune algorithm for predictive and reactive scheduling," Journal of Scheduling, Springer, vol. 20(2), pages 165-182, April.
    3. Herroelen, Willy & Leus, Roel, 2005. "Project scheduling under uncertainty: Survey and research potentials," European Journal of Operational Research, Elsevier, vol. 165(2), pages 289-306, September.
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    Cited by:

    1. Adrian Kampa & Iwona Paprocka, 2021. "Analysis of Energy Efficient Scheduling of the Manufacturing Line with Finite Buffer Capacity and Machine Setup and Shutdown Times," Energies, MDPI, vol. 14(21), pages 1-25, November.
    2. Sinisterra, Wilfrido Quiñones & Lima, Victor Hugo Resende & Cavalcante, Cristiano Alexandre Virginio & Aribisala, Adetoye Ayokunle, 2023. "A delay-time model to integrate the sequence of resumable jobs, inspection policy, and quality for a single-component system," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

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