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A Review of Robust Operations Management under Model Uncertainty
Author(s) -
Lu Mengshi,
Shen ZuoJun Max
Publication year - 2021
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/poms.13239
Subject(s) - robust optimization , computer science , operations research , preference , management science , explosive material , robustness (evolution) , risk analysis (engineering) , economics , mathematical optimization , business , microeconomics , engineering , mathematics , biochemistry , chemistry , organic chemistry , gene
Over the past two decades, there has been explosive growth in the application of robust optimization in operations management (robust OM), fueled by both significant advances in optimization theory and a volatile business environment that has led to rising concerns about model uncertainty. We review some common modeling frameworks in robust OM, including the representation of uncertainty and the decision‐making criteria, and sources of model uncertainty that have arisen in the literature, such as demand, supply, and preference. We discuss the successes of robust OM in addressing model uncertainty, enriching decision criteria, generating structural results, and facilitating computation. We also discuss several future research opportunities and challenges.