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Predictive Global Sensitivity Analysis: Foundational Concepts, Tools, and Applications

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  • Charles L. Munson
  • Lan Luo
  • Xiaohui Huang

Abstract

Modern managers must sift through huge data overload to make quick decisions in dynamic environments. Predictive Global Sensitivity Analysis (PGSA) represents a statistical approach to simplifying a complicated mathematical optimization model into a straightforward set of predictive equations by summarizing numerous complexities into a few highly explanatory variables. Managers can use such equations to make swift decisions with colleagues or customers in real time, or the equations can be used as a monitoring tool to verify current decisions as external conditions change.In this monograph, the authors review the published applications of PGSA that have emerged over the past two decades. Differences in the published works illustrate the underlying flexible nature of the method. Modelers get to practice significant judgement all throughout the process, from application selection through model validation. Section 3 provides a step-by-step tutorial of the full PGSA process. The authors describe how each step has been addressed in the literature to date, and they illustrate each step in detail using two new applications of classic problems in operations research. Section 4 introduces a brand-new application of PGSA that predicts which among three centralized purchasing scenarios that a newly introduced product purchased at a local site should adopt.

Suggested Citation

  • Charles L. Munson & Lan Luo & Xiaohui Huang, 2024. "Predictive Global Sensitivity Analysis: Foundational Concepts, Tools, and Applications," Foundations and Trends(R) in Technology, Information and Operations Management, now publishers, vol. 17(4), pages 235-339, March.
  • Handle: RePEc:now:fnttom:0200000113
    DOI: 10.1561/0200000113
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    References listed on IDEAS

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    2. Harvey M. Wagner, 1995. "Global Sensitivity Analysis," Operations Research, INFORMS, vol. 43(6), pages 948-969, December.
    3. Zhili Tian & Panos Kouvelis & Charles L. Munson, 2015. "Understanding and managing product line complexity: Applying sensitivity analysis to a large-scale MILP model to price and schedule new customer orders," IISE Transactions, Taylor & Francis Journals, vol. 47(4), pages 307-328, April.
    4. Vagstad, Steinar, 2000. "Centralized vs. decentralized procurement: Does dispersed information call for decentralized decision-making?," International Journal of Industrial Organization, Elsevier, vol. 18(6), pages 949-963, August.
    5. Sundaram,Rangarajan K., 1996. "A First Course in Optimization Theory," Cambridge Books, Cambridge University Press, number 9780521497190, June.
    6. Sundaram,Rangarajan K., 1996. "A First Course in Optimization Theory," Cambridge Books, Cambridge University Press, number 9780521497701, June.
    7. David Simchi-Levi & William Schmidt & Yehua Wei & Peter Yun Zhang & Keith Combs & Yao Ge & Oleg Gusikhin & Michael Sanders & Don Zhang, 2015. "Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain," Interfaces, INFORMS, vol. 45(5), pages 375-390, October.
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