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Simulation Optimization for MRO Systems Operations

Author

Listed:
  • Pai Liu

    (Department of Industrial Engineering & Management, Peking University, Beijing 100871, P. R. China)

  • Xi Zhang

    (Department of Industrial Engineering & Management, Peking University, Beijing 100871, P. R. China)

  • Zhongshun Shi

    (Department of Industrial Engineering & Management, Peking University, Beijing 100871, P. R. China)

  • Zewen Huang

    (Department of Industrial Engineering & Management, Peking University, Beijing 100871, P. R. China)

Abstract

In this paper, we address the scheduling issues in a class of maintenance, repair and overhaul systems. By considering all key characteristics such as disassembly, material recovery uncertainty, material matching requirements, stochastic routings and variable processing times, the scheduling problem is formulated into a simulation optimization problem. To solve this difficult problem, we developed two hybrid algorithms based on nested partitions method and optimal computing budged allocation technology. Asymptotic convergence of these two algorithms is proved and numerical results show that the proposed algorithms can generate high quality solutions which outperform the performance of many heuristics.

Suggested Citation

  • Pai Liu & Xi Zhang & Zhongshun Shi & Zewen Huang, 2017. "Simulation Optimization for MRO Systems Operations," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(02), pages 1-23, April.
    • Handle: RePEc:wsi:apjorx:v:34:y:2017:i:02:n:s0217595917500038
    DOI: 10.1142/S0217595917500038
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    References listed on IDEAS

    as
    1. Jie Xu & Si Zhang & Edward Huang & Chun-Hung Chen & Loo Hay Lee & Nurcin Celik, 2016. "MO2TOS: Multi-Fidelity Optimization with Ordinal Transformation and Optimal Sampling," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(03), pages 1-26, June.
    2. Reményi, Christoph & Staudacher, Stephan, 2014. "Systematic simulation based approach for the identification and implementation of a scheduling rule in the aircraft engine maintenance," International Journal of Production Economics, Elsevier, vol. 147(PA), pages 94-107.
    3. Min-Geun Kim & Jae-Min Yu & Dong-Ho Lee, 2015. "Scheduling algorithms for remanufacturing systems with parallel flow-shop-type reprocessing lines," International Journal of Production Research, Taylor & Francis Journals, vol. 53(6), pages 1819-1831, March.
    4. Jie Xu & Edward Huang & Chun-Hung Chen & Loo Hay Lee, 2015. "Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 32(03), pages 1-34.
    5. Nawaz, Muhammad & Enscore Jr, E Emory & Ham, Inyong, 1983. "A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem," Omega, Elsevier, vol. 11(1), pages 91-95.
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    Cited by:

    1. Lingxuan Liu & Leyuan Shi, 2019. "Simulation Optimization on Complex Job Shop Scheduling with Non-Identical Job Sizes," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(05), pages 1-26, October.

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