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Joint optimization of production scheduling and group preventive maintenance planning in multi-machine systems

Author

Listed:
  • Aseem K. Mishra

    (Birla Institute of Management Technology)

  • Divya Shrivastava

    (Shiv Nadar University)

  • Devesh Tarasia

    (Northeastern University)

  • Abdur Rahim

    (University of New Brunswick)

Abstract

This paper addresses the joint optimization model of production scheduling, work-in-process (WIP) inventory control and group preventive maintenance (PM) planning in a multi-machine system with multi-components. The objective is to obtain optimum production sequence, PM intervals and grouping of components, which minimize the total expected cost per unit time of the system. A new meta-heuristic named Jaya algorithm and two popular algorithms viz. simulated annealing (SA) and particle swarm optimization (PSO) are applied to optimize the objective function. Initially, the optimum group of components is obtained based on the integer multiples of individual PM intervals. Secondly, the job permutation sequence incorporating group PM intervals is identified with the largest order value (LOV) rule. The shift in optimum PM intervals is realized with an advanced-postpone balancing approach. Computational results reveal that the proposed integrated model along with group PM yields up to 25% cost reductions when compared to the integrated model with individual maintenance as well as 37% savings while no integration is performed. Furthermore, the performance of algorithms is evaluated with large-sized problems. The obtained results show that Jaya and SA yielded comparable results, however, PSO is least productive. Thus, the proposed approach yields better economic performance and brings more improvised solutions as compared to the conventional methods of integrated scheduling and maintenance optimization problems.

Suggested Citation

  • Aseem K. Mishra & Divya Shrivastava & Devesh Tarasia & Abdur Rahim, 2022. "Joint optimization of production scheduling and group preventive maintenance planning in multi-machine systems," Annals of Operations Research, Springer, vol. 316(1), pages 401-444, September.
    • Handle: RePEc:spr:annopr:v:316:y:2022:i:1:d:10.1007_s10479-021-04362-z
    DOI: 10.1007/s10479-021-04362-z
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    References listed on IDEAS

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    1. Pham, Hoang & Wang, Hongzhou, 1996. "Imperfect maintenance," European Journal of Operational Research, Elsevier, vol. 94(3), pages 425-438, November.
    2. Zhou, Xiaojun & Lu, Zhiqiang & Xi, Lifeng, 2012. "Preventive maintenance optimization for a multi-component system under changing job shop schedule," Reliability Engineering and System Safety, Elsevier, vol. 101(C), pages 14-20.
    3. Jae-Hak Lim & Dong Ho Park, 2007. "Optimal Periodic Preventive Maintenance Schedules With Improvement Factors Depending On Number Of Preventive Maintenances," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 24(01), pages 111-124.
    4. Kerem Bülbül & Philip Kaminsky & Candace Yano, 2004. "Flow shop scheduling with earliness, tardiness, and intermediate inventory holding costs," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(3), pages 407-445, April.
    5. Xiao, Lei & Song, Sanling & Chen, Xiaohui & Coit, David W., 2016. "Joint optimization of production scheduling and machine group preventive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 146(C), pages 68-78.
    6. De, Arijit & Mogale, D.G. & Zhang, Mengdi & Pratap, Saurabh & Kumar, Sri Krishna & Huang, George Q., 2020. "Multi-period multi-echelon inventory transportation problem considering stakeholders behavioural tendencies," International Journal of Production Economics, Elsevier, vol. 225(C).
    7. Sandeep Kumar & Bhushan S. Purohit & Vikas Manjrekar & Vivek Singh & Bhupesh Kumar Lad, 2018. "Investigating the value of integrated operations planning: A case-based approach from automotive industry," International Journal of Production Research, Taylor & Francis Journals, vol. 56(22), pages 6971-6992, November.
    Full references (including those not matched with items on IDEAS)

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