A Review of Robust Operations Management under Model Uncertainty

• 成果类型: Review
• 署名作者: Lu, Mengshi; Shen, Zuo-Jun Max
• 署名单位: Purdue University System; Purdue University; University of California System; University of California Berkeley; University of California System; University of California Berkeley
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13239
• 发表日期: 2021
• 页码: 1927-1943
• 关键词: Operations Management; robust optimization; model uncertainty

摘要

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.