A Review of Robust Operations Management under Model Uncertainty

Lu, Mengshi; Shen, Zuo‐Jun Max

doi:10.1111/poms.13239

Cited by 41 publications

(15 citation statements)

References 226 publications

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“…These include finance (Pflug and Wozabal [302]), environment and energy systems (Park and Bayraksan [294], Zhao and Jiang [444]), scheduling and project management (Jiang et al [216], Natarajan et al [276]), and network flow and transportation (Ahipaşaoğlu et al [2], Carlsson et al [77]). Resorting to real data, further development of data-driven DRO models in emerging real-world applications and public good areas, such as humanitarian logistics and operations management (Lu and Shen [257]), is interesting.…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Frameworks and Results in Distributionally Robust Optimization

Rahimian¹,

Mehrotra²

2022

Open Journal of Mathematical Optimization

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The concepts of risk aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. The statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these concepts. A modeling framework, called distributionally robust optimization (DRO), has recently received significant attention in both the operations research and statistical learning communities. This paper surveys main concepts and contributions to DRO, and relationships with robust optimization, risk aversion, chance-constrained optimization, and function regularization. Various approaches to model the distributional ambiguity and their calibrations are discussed. The paper also describes the main solution techniques used to the solve the resulting optimization problems.

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Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Frameworks and Results in Distributionally Robust Optimization

Rahimian¹,

Mehrotra²

2022

Open Journal of Mathematical Optimization

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“…However, Chu et al (2019) study the case of supply quantity uncertainty with a single supplier, whereas we consider delivery date uncertainty with supplier selection. We refer the interested reader to ( Lu & Shen, 2020 ) for a recent review on the application of robust optimization to operation management problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust optimization approaches for purchase planning with supplier selection under lead time uncertainty

Thevenin

Ben-Ammar²,

Brahimi

2022

European Journal of Operational Research

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“…Flexibility of uncertainty sets and scaleability of solution methods also make RO very attractive for applicational purposes and it has been widely applied to many operations management problems (c.f. Lu and Shen, 2021).…”

Section: Introductionmentioning

confidence: 99%

Designing Tractable Piecewise Affine Policies for Multi-Stage Adjustable Robust Optimization

Thomä¹,

Walther²,

Schiffer³

2022

Preprint

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We study piecewise affine policies for multi-stage adjustable robust optimization (ARO) problems with non-negative right-hand side uncertainty. First, we construct new dominating uncertainty sets and show how a multi-stage ARO problem can be solved efficiently when uncertainty is replaced by these new sets. We then demonstrate how solutions for this alternative problem can be transformed to solutions for the original problem. By carefully choosing the dominating sets, we prove strong approximation bounds for our policies and extend many previously best known bounds for the twostaged problem variant to its multi-stage counterpart. In two numerical experiments we identify beneficial and disadvantageous properties for our policies and present effective adjustments to tackle the most critical disadvantages. Overall, the experiments show that our piecewise affine policies can be computed by orders of magnitude faster than affine policies, while often yielding comparable or even better results.

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A Review of Robust Operations Management under Model Uncertainty

Abstract: This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Cited by 41 publications

References 226 publications

Frameworks and Results in Distributionally Robust Optimization

Frameworks and Results in Distributionally Robust Optimization

Robust optimization approaches for purchase planning with supplier selection under lead time uncertainty

Designing Tractable Piecewise Affine Policies for Multi-Stage Adjustable Robust Optimization

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