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The Analytics of Robust Satisficing: Predict, Optimize, Satisfice, Then Fortify

Melvyn Sim¹; Qinshen Tang²; Minglong Zhou³; Taozeng Zhu⁴

¹ Department of Analytics & Operations (DAO), NUS Business School, National University of Singapore, Singapore 119245 · ² Division of Information Technology and Operations Management, Nanyang Business School, Nanyang Technological University, Singapore 639956 · ³ Department of Management Science, School of Management, Fudan University, Shanghai 200433, China; · ⁴ Institute of Supply Chain Analytics, Dongbei University of Finance and Economics, Dalian 116025, China

In the paper, “The Analytics of Robust Satisficing: Predict, Optimize, Satisfice, Then Fortify,” published in Operations Research, authors Sim, Tang, Zhou, and Zhu introduce a novel approach to decision making under uncertainty. Their method, termed “estimation-fortified robust satisficing,” leverages advanced predictive and prescriptive analytics to optimize decisions where traditional models falter due to risk ambiguity and estimation uncertainties. This approach not only enhances the resilience of decisions against unforeseen variations but also consistently outperforms conventional predictive methods in scenarios characterized by sparse data. This significant advancement promises to fortify decision-making processes in critical sectors such as finance and operations management, offering a new paradigm in handling the inherent uncertainties of real-world systems.

DOI

10.1287/opre.2023.0199

Volume

73 (5)

Pages

2708-2728

Language

en

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