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A simulation-based multi-objective optimization framework: A case study on inventory management

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  • Tsai, Shing Chih
  • Chen, Sin Ting

Abstract

We propose a simulation-based solution framework for tackling the multi-objective inventory optimization problem. The goal is to find appropriate settings of reorder point and order quantity to minimize three objective functions simultaneously, which are the expected values of the total inventory cost, the average inventory level, and the frequency of inventory shortage. We develop new algorithms that can exploit statistically valid ranking and selection (R&S) procedures and the desirable mechanics of conventional multi-objective optimization techniques. Two simulation algorithms are proposed to be applied in different scenarios depending on the preference information that is revealed either during or after the actual optimization process. Experimental results are provided to evaluate the efficiency of the developed algorithms and other existing solution frameworks.

Suggested Citation

  • Tsai, Shing Chih & Chen, Sin Ting, 2017. "A simulation-based multi-objective optimization framework: A case study on inventory management," Omega, Elsevier, vol. 70(C), pages 148-159.
    • Handle: RePEc:eee:jomega:v:70:y:2017:i:c:p:148-159
    DOI: 10.1016/j.omega.2016.09.007
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    References listed on IDEAS

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    1. Shing Chih Tsai, 2013. "Rapid Screening Procedures for Zero-One Optimization via Simulation," INFORMS Journal on Computing, INFORMS, vol. 25(2), pages 317-331, May.
    2. Tsai, Shing Chih & Zheng, Ya-Xin, 2013. "A simulation optimization approach for a two-echelon inventory system with service level constraints," European Journal of Operational Research, Elsevier, vol. 229(2), pages 364-374.
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    3. Cheng, Zhenxia & Luo, Jun & Wu, Ruijing, 2023. "On the finite-sample statistical validity of adaptive fully sequential procedures," European Journal of Operational Research, Elsevier, vol. 307(1), pages 266-278.
    4. Avci, Mualla Gonca & Selim, Hasan, 2018. "A multi-objective simulation-based optimization approach for inventory replenishment problem with premium freights in convergent supply chains," Omega, Elsevier, vol. 80(C), pages 153-165.
    5. Shing Chih Tsai & Wu Hung Lin & Chia Cheng Wu & Shao Jen Weng & Ching Fen Tang, 2022. "Decision support algorithms for optimizing surgery start times considering the performance variation," Health Care Management Science, Springer, vol. 25(2), pages 208-221, June.
    6. Dai, Bo & Chen, Haoxun & Li, Yuan & Zhang, Yidong & Wang, Xiaoqing & Deng, Yuming, 2023. "An alternating direction method of multipliers for optimizing (s, S) policies in a distribution system with joint replenishment volume constraints," Omega, Elsevier, vol. 116(C).
    7. Tsionas, Mike G., 2018. "A Bayesian approach to find Pareto optima in multiobjective programming problems using Sequential Monte Carlo algorithms," Omega, Elsevier, vol. 77(C), pages 73-79.
    8. Nguyen, Duy Tan & Adulyasak, Yossiri & Landry, Sylvain, 2021. "Research manuscript: The Bullwhip Effect in rule-based supply chain planning systems–A case-based simulation at a hard goods retailer," Omega, Elsevier, vol. 98(C).
    9. van Schilt, Isabelle M. & van Kalker, Jonna & Lefter, Iulia & Kwakkel, Jan H. & Verbraeck, Alexander, 2024. "Buffer scheduling for improving on-time performance and connectivity with a multi-objective simulation–optimization model: A proof of concept for the airline industry," Journal of Air Transport Management, Elsevier, vol. 115(C).
    10. Federico Toffano & Michele Garraffa & Yiqing Lin & Steven Prestwich & Helmut Simonis & Nic Wilson, 2022. "A multi-objective supplier selection framework based on user-preferences," Annals of Operations Research, Springer, vol. 308(1), pages 609-640, January.

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