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Monte Carlo Enhancement via Simulation Decomposition: A “Must-Have” Inclusion for Many Disciplines

Author

Listed:
  • Mariia Kozlova

    (School of Business and Management, LUT University, 53850 Lappeenranta, Finland)

  • Julian Scott Yeomans

    (Operations Management and Information Systems Area, Schulich School of Business, York University, Toronto, Ontario M3J 1P3, Canada)

Abstract

Monte Carlo (MC) simulation is widely used in many different disciplines in order to analyze problems that involve uncertainty. Simulation decomposition has recently provided a simple, but powerful, advancement to the standard Monte Carlo approach. Its value for better informing decision making has been previously shown in the investment-analysis field. In this paper, we demonstrate that simulation decomposition can enhance problem analysis in a wide array of domains by applying it to three very different disciplines: geology, business, and environmental science. Further extensions to such disciplines as engineering, natural sciences, and social sciences are discussed. We propose that by incorporating simulation decomposition into pedagogical practices, we expect students to significantly advance their problem-understanding and problem-solving skills.

Suggested Citation

  • Mariia Kozlova & Julian Scott Yeomans, 2022. "Monte Carlo Enhancement via Simulation Decomposition: A “Must-Have” Inclusion for Many Disciplines," INFORMS Transactions on Education, INFORMS, vol. 22(3), pages 147-159, May.
    • Handle: RePEc:inm:orited:v:22:y:2022:i:3:p:147-159
    DOI: 10.1287/ited.2019.0240
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    References listed on IDEAS

    as
    1. M. Kozlova & M. Collan & P. Luukka, 2016. "Simulation Decomposition: New Approach For Better Simulation Analysis Of Multi-Variable Investment Projects," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 21(2), pages 3-18, November.
    2. M. Kozlova & M. Collan & P. Luukka, 2016. "Simulation Decomposition: New Approach For Better Simulation Analysis Of Multi-Variable Investment Projects," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 21(2), pages 3-18, November.
    3. Enrico Zio, 2013. "System Reliability and Risk Analysis," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 7-17, Springer.
    4. Ivan Deviatkin & Musharof Khan & Elizabeth Ernst & Mika Horttanainen, 2019. "Wooden and Plastic Pallets: A Review of Life Cycle Assessment (LCA) Studies," Sustainability, MDPI, vol. 11(20), pages 1-17, October.
    5. Enrico Zio, 2013. "Monte Carlo Simulation: The Method," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 19-58, Springer.
    6. Enrico Zio, 2013. "The Monte Carlo Simulation Method for System Reliability and Risk Analysis," Springer Series in Reliability Engineering, Springer, edition 127, number 978-1-4471-4588-2, August.
    7. Enrico Zio, 2013. "System Reliability and Risk Analysis by Monte Carlo Simulation," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 59-81, Springer.
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