Simulation-Based Optimization: A Comprehensive Review of Concept, Method and Its Application

Authors

  • Milad Abolghasemian * Depaetment of Industrial Engineering, Ayandegan University, Tonekabon, Iran https://orcid.org/0000-0002-1341-7855
  • Sedigheh Kaveh Department of Computer Engineering, Ayandegan University, Tonekabon, Iran
  • Fatemeh Ebrahimzadeh Department of Computer Engineering, Ayandegan University, Tonekabon, Iran

https://doi.org/10.22105/raise.vi.76

Abstract

Simulation-Based Optimization (SBO), as a hybrid approach combining simulation modeling and optimization techniques, is a powerful tool for solving complex decision-making problems that cannot, or cannot be reliably, solved by classical methods due to uncertainty, nonlinear and discrete behaviors, high dimensions, and the black-box nature of systems. The combination of simulation's descriptive power and optimization's prescriptive power enables accurate analysis of dynamic, uncertain environments and the identification of optimal or near-optimal decision-making policies. This article provides a comprehensive overview of the fundamental concepts, classification of approaches, and key methods in the field of SBO. In this regard, a variety of optimization methods used alongside simulation—including deterministic and stochastic methods, metaheuristics, machine learning-based approaches, multiobjective frameworks, and constrained optimization techniques—are reviewed. Special attention is paid to derivative-free methods and surrogates, which are common for optimizing expensive, noisy, and non-differentiable models. The role of various simulation approaches, such as discrete-event, continuous-time, agent-based, and Monte Carlo simulations, in shaping the SBO landscape is also discussed. In the applications section, the paper reviews key areas including Supply Chain Management (SCM), healthcare systems, transportation and logistics, energy and environment, and military and defense applications. For each area, it is shown how SBO can improve strategic and operational decision-making under uncertainty, enhance system performance, and increase its resilience. In addition, the significant challenges of SBO, including high computational cost, model uncertainty, data limitations, and high dimensionality, are analyzed. Finally, the article highlights emerging trends, including the integration of machine learning and simulation, the development of digital twins, the use of high-performance computing, and the move towards real-time optimization. Overall, this review aims to provide a comprehensive overview of the theoretical foundations, methodological advances, practical applications, and future research directions in SBO.

Keywords:

Simulation, Optimization, Simulation-Based optimization

References

  1. [1] Jahangiri, S., Abolghasemian, M., Ghasemi, P., & Chobar, A. P. (2023). Simulation-based optimisation: Analysis of the emergency department resources under COVID-19 conditions. International journal of industrial and systems engineering, 43(1), 1–19. https://doi.org/10.1504/IJISE.2023.128399
  2. [2] Abolghasemian, M., Ghane Kanafi, A., & Daneshmandmehr, M. (2020). A two-phase simulation-based optimization of the hauling system in open-pit mine. Interdisciplinary journal of management studies, 13(4), 705–732. https://doi.org/10.22059/ijms.2020.294809.673898
  3. [3] Jahangiri, S., Abolghasemian, M., Pourghader Chobar, A., Nadaffard, A., & Mottaghi, V. (2021). Ranking of key resources in the humanitarian supply chain in the emergency department of iranian hospital: A real case study in COVID-19 conditions. Journal of applied research on industrial engineering, 8(Special Issue), 1–10. https://doi.org/10.22105/jarie.2021.275255.1263
  4. [4] Abolghasemian, M., Pourghader Chobar, A., AliBakhshi, M., Fakhr, A., & Moradi Pirbalouti, S. (2021). Delay scheduling based on discrete-event simulation for construction projects. Iranian journal of operations research, 12(1), 49–63. https://www.researchgate.net/publication/359146227
  5. [5] Abolghasemian, M., Eskandari, H., & Darabi, H. (2018). Simulation based optimization of haulage system of an open-pit mine: Meta modeling approach. Organizational resources management researches, 8(2), 1–17. (In Persian). https://dor.isc.ac/dor/20.1001.1.22286977.1397.8.2.2.6
  6. [6] Hemmati, A., Kaveh, F., Abolghasemian, M., & Pourghader Chobar, A. (2024). Simulating the line balance to provide an improvement plan for optimal production and costing in petrochemical industries. Engineering management and soft computing, 10(1), 190–212. https://doi.org/10.22091/jemsc.2024.11189.1198
  7. [7] Balasubramanian, S., Shukla, V., Islam, N., Upadhyay, A., & Duong, L. (2025). Applying artificial intelligence in healthcare: Lessons from the COVID-19 pandemic. International journal of production research, 63(2), 594–627. https://doi.org/10.1080/00207543.2023.2263102
  8. [8] Shadkam, E., & Irannezhad, E. (2025). A comprehensive review of simulation optimization methods in agricultural supply chains and transition towards an agent-based intelligent digital framework for agriculture 4.0. Engineering applications of artificial intelligence, 143, 109930. https://doi.org/10.1016/j.engappai.2024.109930
  9. [9] Lau, Y. Y., Dulebenets, M. A., Yip, H. T., & Tang, Y. M. (2022). Healthcare supply chain management under COVID-19 settings: The existing practices in Hong Kong and the United States. Healthcare, 10(8), 1–19. https://doi.org/10.3390/healthcare10081549
  10. [10] Vaseei, M., Daneshmand-Mehr, M., Bazrafshan, M., & Kanafi, A. G. (2024). A network data envelopment analysis to evaluate the performance of a sustainable supply chain using bootstrap simulation. Journal of engineering research, 12(4), 904–915. https://doi.org/10.1016/j.jer.2023.10.003
  11. [11] Chen, Y., Bayanati, M., Ebrahimi, M., & Khalijian, S. (2022). A novel optimization approach for educational class scheduling with considering the students and teachers’ preferences. Discrete dynamics in nature and society, 2022(1), 5505631. https://doi.org/10.1155/2022/5505631
  12. [12] Silalahi, S. A., Pujawan, I. N., & Singgih, M. L. (2025). Agent-based simulation of digital interoperability thresholds in fragmented air cargo systems: Evidence from a developing country. Logistics, 9(4), 1–18. https://doi.org/10.3390/logistics9040160
  13. [13] Moradi Afrapoli, A., & Askari-Nasab, H. (2019). Mining fleet management systems: a review of models and algorithms. International journal of mining, reclamation and environment, 33(1), 42–60. https://doi.org/10.1080/17480930.2017.1336607
  14. [14] Akhtari, S., & Sowlati, T. (2020). Hybrid optimization-simulation for integrated planning of bioenergy and biofuel supply chains. Applied energy, 259, 114124. https://doi.org/10.1016/j.apenergy.2019.114124
  15. [15] Alarie, S., & Gamache, M. (2002). Overview of solution strategies used in truck dispatching systems for open pit mines. International journal of surface mining, reclamation and environment, 16(1), 59–76. https://doi.org/10.1076/ijsm.16.1.59.3408
  16. [16] Amaran, S., Sahinidis, N. V, Sharda, B., & Bury, S. J. (2016). Simulation optimization: A review of algorithms and applications. Annals of operations research, 240(1), 351–380. https://doi.org/10.1007/s10479-015-2019-x
  17. [17] Barnes, R. J., King, M. S., & Johnson, T. B. (1979). Probability techniques for analyzing open pit production systems. Contribution to conference (pp. 462-476). Minnesota. https://experts.umn.edu/en/publications/probability-techniques-for-analyzing-open-pit-production-systems/
  18. [18] Barton, R. R. (2009). Simulation optimization using meta-models. Proceedings of the 2009 winter simulation conference (WSC) (pp. 230–238). IEEE. https://doi.org/10.1109/WSC.2009.5429328
  19. [19] Chugh, T., Sindhya, K., Hakanen, J., & Miettinen, K. (2019). A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms. Soft computing, 23(9), 3137–3166. https://doi.org/10.1007/s00500-017-2965-0
  20. [20] Barton, R. R., & Meckesheimer, M. (2006). Metamodel-based simulation optimization. In Handbooks in operations research and management science: Simulation (pp. 535–574). Elsevier. https://doi.org/10.1016/S0927-0507(06)13018-2
  21. [21] Curry, J. A., Ismay, M. J. L., & Jameson, G. J. (2014). Mine operating costs and the potential impacts of energy and grinding. Minerals engineering, 56, 70–80. https://doi.org/10.1016/j.mineng.2013.10.020
  22. [22] Dengiz, B., & Belgin, O. (2014). Simulation optimization of a multi-stage multi-product paint shop line with Response Surface Methodology. Simulation, 90(3), 265–274. https://doi.org/10.1177/0037549713516508
  23. [23] Dengiz, B., Bektas, T., & Ultanir, A. E. (2006). Simulation optimization based DSS application: A diamond tool production line in industry. Simulation modelling practice and theory, 14(3), 296–312. https://doi.org/10.1016/j.simpat.2005.07.001
  24. [24] Dengiz, B., İç, Y. T., & Belgin, O. (2016). A meta-model based simulation optimization using hybrid simulation-analytical modeling to increase the productivity in automotive industry. Mathematics and computers in simulation, 120, 120–128. https://doi.org/10.1016/j.matcom.2015.07.005
  25. [25] Fu, M. C. (2002). Optimization for simulation: Theory vs. practice. Informs journal on computing, 14(3), 192–215. https://doi.org/10.1287/ijoc.14.3.192.113
  26. [26] Glover, F., Kelly, J. P., & Laguna, M. (1996). New advances and applications of combining simulation and optimization. Proceedings of the 28th conference on winter simulation (pp. 144–152). IEEE Computer Society. https://doi.org/10.1145/256562.256595
  27. [27] He, M. X., Wei, J. C., Lu, X. M., & Huang, B. X. (2010). The genetic algorithm for truck dispatching problems in surface mine. Information technology journal, 9(4), 710–714. https://doi.org/10.3923/itj.2010.710.714
  28. [28] Jerbi, A., Ammar, A., Krid, M., & Salah, B. (2019). Performance optimization of a flexible manufacturing system using simulation: The Taguchi method versus OptQuest. Simulation, 95(11), 1085–1096. https://doi.org/10.1177/0037549718819804
  29. [29] Kleijnen, J. P. C., & Sargent, R. G. (2000). A methodology for fitting and validating meta-models in simulation1Two anonymous referees’ comments on the first draft lead to an improved organization of our paper. European journal of operational research, 120(1), 14–29. https://doi.org/10.1016/S0377-2217(98)00392-0
  30. [30] Law, A. M. (2007). Simulation modeling and analysis. Mcgraw-hill New York. https://www.amazon.com/Simulation-Mcgraw-hill-Industrial-Engineering-Management/dp/0073401323
  31. [31] Mena, R., Zio, E., Kristjanpoller, F., & Arata, A. (2013). Availability-based simulation and optimization modeling framework for open-pit mine truck allocation under dynamic constraints. International journal of mining science and technology, 23(1), 113–119. https://doi.org/10.1016/j.ijmst.2013.01.017
  32. [32] Moniri-Morad, A., Pourgol-Mohammad, M., Aghababaei, H., & Sattarvand, J. (2019). A methodology for truck allocation problems considering dynamic circumstances in open pit mines, case study of the Sungun copper mine. Mining-geology-petroleum journal, 34(4), 57–65. https://doi.org/10.17794/rgn.2019.4.6
  33. [33] Ozdemir, B., & Kumral, M. (2019). Simulation-based optimization of truck-shovel material handling systems in multi-pit surface mines. Simulation modelling practice and theory, 95, 36–48. https://doi.org/10.1016/j.simpat.2019.04.006
  34. [34] Shishvan, M. S., & Benndorf, J. (2019). Simulation-based optimization approach for material dispatching in continuous mining systems. European journal of operational research, 275(3), 1108–1125. https://doi.org/10.1016/j.ejor.2018.12.015
  35. [35] Upadhyay, S. P., & Askari-Nasab, H. (2018). Simulation and optimization approach for uncertainty-based short-term planning in open pit mines. International journal of mining science and technology, 28(2), 153–166. https://doi.org/10.1016/j.ijmst.2017.12.003
  36. [36] Zeinali, F., Mahootchi, M., & Sepehri, M. M. (2015). Resource planning in the emergency departments: A simulation-based metamodeling approach. Simulation modelling practice and theory, 53, 123–138. https://doi.org/10.1016/j.simpat.2015.02.002
  37. [37] Zhang, L., & Xia, X. (2015). An integer programming approach for truck-shovel dispatching problem in open-pit mines. Energy procedia, 75, 1779–1784. https://doi.org/10.1016/j.egypro.2015.07.469
  38. [38] Abolghasemian, M., Kanafi, A. G., & Daneshmand-Mehr, M. (2022). Simulation-based multiobjective optimization of open-pit mine haulage system: A modified-NBI method and meta modeling approach. Complexity, 2022(1), 3540736. https://doi.org/10.1155/2022/3540736

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Published

2025-06-29

Issue

Vol. 2 No. 3 (2025): Research Annals of Industrial and Systems Engineering

Section

Articles

How to Cite

Abolghasemian, M., Kaveh, S., & Ebrahimzadeh, F. (2025). Simulation-Based Optimization: A Comprehensive Review of Concept, Method and Its Application. Research Annals of Industrial and Systems Engineering, 2(3), 196-208. https://doi.org/10.22105/raise.vi.76

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