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Simulation optimization in inventory replenishment: a classification

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  • Hamed Jalali
  • Inneke Van Nieuwenhuyse

Abstract

Simulation optimization is increasingly popular for solving complicated and mathematically intractable business problems. Focusing on academic articles published between 1998 and 2013, the present survey aims to unveil the extent to which simulation optimization has been used to solve practical inventory problems (as opposed to small, theoretical “toy problem”), and to detect any trends that might have arisen (e.g., popular topics, effective simulation optimization methods, frequently studied inventory system structures). We find that metaheuristics (especially genetic algorithms) and methods that combine several simulation optimization techniques are the most popular. The resulting categorizations provide a useful overview for researchers studying complex inventory management problems, by providing detailed information on the inventory system characteristics and the employed simulation optimization techniques, highlighting articles that involve stochastic constraints (e.g., expected fill rate constraints) or that employ a robust simulation optimization approach. Finally, in highlighting both trends and gaps in the research field, this review suggests avenues for further research.

Suggested Citation

  • Hamed Jalali & Inneke Van Nieuwenhuyse, 2015. "Simulation optimization in inventory replenishment: a classification," IISE Transactions, Taylor & Francis Journals, vol. 47(11), pages 1217-1235, November.
    • Handle: RePEc:taf:uiiexx:v:47:y:2015:i:11:p:1217-1235
    DOI: 10.1080/0740817X.2015.1019162
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    Citations

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    Cited by:

    1. Noordhoek, Marije & Dullaert, Wout & Lai, David S.W. & de Leeuw, Sander, 2018. "A simulation–optimization approach for a service-constrained multi-echelon distribution network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 292-311.
    2. Pourya Pourhejazy & Oh Kyoung Kwon, 2016. "The New Generation of Operations Research Methods in Supply Chain Optimization: A Review," Sustainability, MDPI, vol. 8(10), pages 1-23, October.
    3. Leonid Mylnikov & Rustam Fayzrakhmanov, 2018. "Production Planning with Parameters on the Basis of Dynamic Predictive Models: Interconnection and the Inertness of their Interaction," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 265-281.
    4. Bo Dai & Fenfen Li, 2021. "Joint Inventory Replenishment Planning of an E-Commerce Distribution System with Distribution Centers at Producers’ Locations," Logistics, MDPI, vol. 5(3), pages 1-14, July.
    5. 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.
    6. Kleijnen, Jack P.C., 2017. "Regression and Kriging metamodels with their experimental designs in simulation: A review," European Journal of Operational Research, Elsevier, vol. 256(1), pages 1-16.
    7. Jalali, Hamed & Van Nieuwenhuyse, Inneke & Picheny, Victor, 2017. "Comparison of Kriging-based algorithms for simulation optimization with heterogeneous noise," European Journal of Operational Research, Elsevier, vol. 261(1), pages 279-301.
    8. Gioia, Daniele Giovanni & Minner, Stefan, 2023. "On the value of multi-echelon inventory management strategies for perishable items with on-/off-line channels," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
    9. Mohammed Hichame Benbitour & Evren Sahin & Yves Dallery, 2019. "The use of rush deliveries in periodic review assemble-to-order systems," Post-Print hal-01997380, HAL.
    10. Romero-Silva, Rodrigo & de Leeuw, Sander, 2021. "Learning from the past to shape the future: A comprehensive text mining analysis of OR/MS reviews," Omega, Elsevier, vol. 100(C).
    11. 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).
    12. Renato Matta & Timothy J. Lowe, 2023. "Product price alignment with seller service rating and consumer satisfaction," Annals of Operations Research, Springer, vol. 320(2), pages 695-725, January.
    13. Othmane Benmoussa, 2022. "Improving Replenishment Flows Using Simulation Results: A Case Study," Logistics, MDPI, vol. 6(2), pages 1-26, May.
    14. Yanyan Yang & Shenle Pan & Eric Ballot, 2017. "Mitigating supply chain disruptions through interconnected logistics services in the Physical Internet," International Journal of Production Research, Taylor & Francis Journals, vol. 55(14), pages 3970-3983, July.
    15. Javad Seif & Mohammad Dehghanimohammadabadi & Andrew Junfang Yu, 2020. "Integrated preventive maintenance and flow shop scheduling under uncertainty," Flexible Services and Manufacturing Journal, Springer, vol. 32(4), pages 852-887, December.
    16. Shishvan, Masoud Soleymani & Benndorf, Jörg, 2019. "Simulation-based optimization approach for material dispatching in continuous mining systems," European Journal of Operational Research, Elsevier, vol. 275(3), pages 1108-1125.
    17. Nihan Kabadayi & Mohammad Dehghanimohammadabadi, 2022. "Multi-objective supplier selection process: a simulation–optimization framework integrated with MCDM," Annals of Operations Research, Springer, vol. 319(2), pages 1607-1629, December.

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