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Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem

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  • Lee, Loo Hay
  • Chew, Ek Peng
  • Teng, Suyan
  • Chen, Yankai

Abstract

Simulation optimization has received considerable attention from both simulation researchers and practitioners. In this study, we develop a solution framework which integrates multi-objective evolutionary algorithm (MOEA) with multi-objective computing budget allocation (MOCBA) method for the multi-objective simulation optimization problem. We apply it on a multi-objective aircraft spare parts allocation problem to find a set of non-dominated solutions. The problem has three features: huge search space, multi-objective, and high variability. To address these difficulties, the solution framework employs simulation to estimate the performance, MOEA to search for the more promising designs, and MOCBA algorithm to identify the non-dominated designs and efficiently allocate the simulation budget. Some computational experiments are carried out to test the effectiveness and performance of the proposed solution framework.

Suggested Citation

  • Lee, Loo Hay & Chew, Ek Peng & Teng, Suyan & Chen, Yankai, 2008. "Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem," European Journal of Operational Research, Elsevier, vol. 189(2), pages 476-491, September.
    • Handle: RePEc:eee:ejores:v:189:y:2008:i:2:p:476-491
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    3. Sleptchenko, Andrei & Turan, Hasan Hüseyin & Pokharel, Shaligram & ElMekkawy, Tarek Y., 2019. "Cross-training policies for repair shops with spare part inventories," International Journal of Production Economics, Elsevier, vol. 209(C), pages 334-345.
    4. Ito, Kodo & Mizutani, Satoshi & Nakagawa, Toshio, 2020. "Optimal inspection models with minimal repair," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    5. Goh, C.K. & Tan, K.C. & Liu, D.S. & Chiam, S.C., 2010. "A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design," European Journal of Operational Research, Elsevier, vol. 202(1), pages 42-54, April.
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    7. Wang, Yujia & Yang, Yupu, 2010. "Particle swarm with equilibrium strategy of selection for multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 200(1), pages 187-197, January.
    8. Kyle Cooper & Susan R. Hunter, 2020. "PyMOSO: Software for Multiobjective Simulation Optimization with R-PERLE and R-MinRLE," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 1101-1108, October.
    9. Miranda, Rafael de Carvalho & Montevechi, José Arnaldo Barra & da Silva, Aneirson Francisco & Marins, Fernando Augusto Silva, 2017. "Increasing the efficiency in integer simulation optimization: Reducing the search space through data envelopment analysis and orthogonal arrays," European Journal of Operational Research, Elsevier, vol. 262(2), pages 673-681.
    10. Lin, Rung-Chuan & Sir, Mustafa Y. & Pasupathy, Kalyan S., 2013. "Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services," Omega, Elsevier, vol. 41(5), pages 881-892.
    11. Regattieri, A. & Giazzi, A. & Gamberi, M. & Gamberini, R., 2015. "An innovative method to optimize the maintenance policies in an aircraft: General framework and case study," Journal of Air Transport Management, Elsevier, vol. 44, pages 8-20.
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    13. Syberfeldt, Anna & Ng, Amos & John, Robert I. & Moore, Philip, 2010. "Evolutionary optimisation of noisy multi-objective problems using confidence-based dynamic resampling," European Journal of Operational Research, Elsevier, vol. 204(3), pages 533-544, August.

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