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Managing Disruptions in a Flow-Shop Manufacturing System

Author

Listed:
  • Ajay Surendrarao Bhongade

    (Department of Mechanical Engineering, Bharati Vidyapeeth College of Engineering, Navi Mumbai 400708, India)

  • Prakash Manohar Khodke

    (Department of Mechanical Engineering, Government College of Engineering, Yavatmal 444604, India)

  • Ateekh Ur Rehman

    (Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh 11451, Saudi Arabia)

  • Manoj Dattatray Nikam

    (Department of Mechanical Engineering, Bharati Vidyapeeth College of Engineering, Navi Mumbai 400708, India)

  • Prathamesh Dattatray Patil

    (Department of Mechanical Engineering, Bharati Vidyapeeth College of Engineering, Navi Mumbai 400708, India)

  • Pramod Suryavanshi

    (Department of Artificial Intelligence, Dublin Business School, D02WC04 Dublin, Ireland)

Abstract

There is a manufacturing system where several parts are processed through machining workstations and later assembled to form final products. In the event of disruptions such as machine failure, the original flow-shop schedule needs to be revised and/or rescheduled. In such a scenario, rescheduling methods based on right-shift rescheduling and affected operations rescheduling work very well. Here in this study, the deviation of the make-span of the revised schedule from the original schedule is used as a performance measure. We have proposed three rescheduling methods. There are multiple factors that influence the performance of the rescheduling methodology. One of them is the make-span deviation of the schedule, and the factors influencing it are optimality of the initial solution, failure duration, deviation of make-span, rescheduling method, size, and instant of failure. The initial schedule and problem size depend on the flow-shop manufacturing system for which scheduling is performed, but the method of rescheduling depends on the decision as to which rescheduling methodology is to be selected. Computations are performed using full factorial experimentation. We also observed that right-shift rescheduling is the preferred rescheduling method in the majority of situations. In contrast, the affected operation rescheduling method is also equally suitable when the initial solution is created using modified bottleneck minimum idleness.

Suggested Citation

  • Ajay Surendrarao Bhongade & Prakash Manohar Khodke & Ateekh Ur Rehman & Manoj Dattatray Nikam & Prathamesh Dattatray Patil & Pramod Suryavanshi, 2023. "Managing Disruptions in a Flow-Shop Manufacturing System," Mathematics, MDPI, vol. 11(7), pages 1-22, April.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1731-:d:1116144
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    References listed on IDEAS

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    1. Young-In Kim & Hyun-Jung Kim, 2021. "Rescheduling of unrelated parallel machines with job-dependent setup times under forecasted machine breakdown," International Journal of Production Research, Taylor & Francis Journals, vol. 59(17), pages 5236-5258, September.
    2. Iracyanne Retto Uhlmann & Renata Mariani Zanella & Enzo Morosini Frazzon, 2022. "Hybrid flow shop rescheduling for contract manufacturing services," International Journal of Production Research, Taylor & Francis Journals, vol. 60(3), pages 1069-1085, February.
    3. Ayten Turkcan & M. Akturk & Robert Storer, 2009. "Predictive/reactive scheduling with controllable processing times and earliness-tardiness penalties," IISE Transactions, Taylor & Francis Journals, vol. 41(12), pages 1080-1095.
    4. G. M. Komaki & Shaya Sheikh & Behnam Malakooti, 2019. "Flow shop scheduling problems with assembly operations: a review and new trends," International Journal of Production Research, Taylor & Francis Journals, vol. 57(10), pages 2926-2955, May.
    5. Pablo Valledor & Alberto Gomez & Paolo Priore & Javier Puente, 2020. "Modelling and Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments," Complexity, Hindawi, vol. 2020, pages 1-17, July.
    6. Yokoyama, Masao, 2008. "Flow-shop scheduling with setup and assembly operations," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1184-1195, June.
    7. Guo, Bo & Nonaka, Yasuo, 1999. "Rescheduling and optimization of schedules considering machine failures," International Journal of Production Economics, Elsevier, vol. 60(1), pages 503-513, April.
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