The Analytics of Robust Satisficing: Predict, Optimize, Satisfice, Then Fortify
Melvyn Sim, Qinshen Tang, Minglong Zhou, Taozeng Zhu
Operations Research · 2025 · Volume 73 · Issue 5
Abstract
We introduce a novel approach to prescriptive analytics that leverages robust satisficing techniques to determine optimal decisions in situations of distribution ambiguity and parameter estimation uncertainty. Our decision model relies on a reward function that incorporates uncertain parameters, which can be predicted using available side information. However, the accuracy of the linear prediction model depends on the quality of regression coefficient estimates derived from the available data. To achieve a desired level of fragility under distribution ambiguity, we begin by solving a residual-based robust satisficing model in which the residuals from the regression are used to construct an estimated empirical distribution and a target is established relative to the predict-then-optimize objective value. In the face of estimation uncertainty, we then solve an estimation-fortified robust satisficing model that minimizes the influence of estimation uncertainty while ensuring that the solution would maintain at most the same level of fragility in achieving a less ambitious guarding target. Our approach is supported by statistical justifications, and we propose tractable models for various scenarios, such as saddle functions, two-stage linear optimization problems, and decision-dependent predictions. We demonstrate the effectiveness of our approach through case studies involving a wine portfolio investment problem and a multiproduct pricing problem using real-world data. Our numerical studies show that our approach outperforms the predict-then-optimize approach in achieving higher expected rewards and at lower risks when evaluated on the actual distribution. Notably, we observe significant improvements over the benchmarks, particularly in cases of limited data availability. Funding: The research of M. Sim was supported by the Ministry of Education, Singapore under its 2019 Academic Research Fund Tier 3 [Grant MOE-2019-T3-1-010]. The research of Q. Tang was supported by Nanyang Technological University [Start-Up Grant 020022-00001] and the Ministry of Education, Singapore [Tier 1 Grant 25010057]. The research of M. Zhou was supported by the National Natural Science Foundation of China [Grants 72301075 and 72293564/72293560]. The research of T. Zhu was supported by the National Natural Science Foundation of China [Grant 72401058]. Any opinions, findings, conclusions, or recommendations expressed in the material are those of the authors and do not necessarily reflect the views of the Ministry of Education, Singapore. Supplementary Material: The online appendix and computer code and data that support the findings of this study are available within this article’s supplemental material at https://doi.org/10.1287/opre.2023.0199 .
Keywords
Optimization, robust optimization, robust satisficing, predictive analytics, prescriptive analytics
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