A Review of Robust Operations Management under Model Uncertainty
A Review of Robust Operations Management under Model Uncertainty
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DOI：
10.1111/poms.13239
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发表时间：
2020-07
影响因子：
5
通讯作者：
Mengshi Lu;Z. Shen
中科院分区：
文献类型：
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作者：
Mengshi Lu;Z. Shen
在过去的二十年中，稳健优化在运营管理中的应用（稳健OM）出现了爆炸性增长，这是由优化理论的重大进步和不稳定的商业环境推动的，这导致了对模型不确定性的担忧。我们回顾了鲁棒OM中的一些常见建模框架，包括不确定性的表示和决策标准，以及文献中出现的模型不确定性的来源，如需求，供应和偏好。我们讨论了强大的OM在解决模型的不确定性，丰富的决策标准，产生结构性的结果，并促进计算的成功。我们还讨论了一些未来的研究机会和挑战。
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.