The Analytics of Robust Satisficing: Predict, Optimize, Satisfice, Then Fortify
Melvyn Sim (),
Qinshen Tang (),
Minglong Zhou () and
Taozeng Zhu ()
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Melvyn Sim: Department of Analytics & Operations (DAO), NUS Business School, National University of Singapore, Singapore 119245
Qinshen Tang: Division of Information Technology and Operations Management, Nanyang Business School, Nanyang Technological University, Singapore 639956
Minglong Zhou: Department of Management Science, School of Management, Fudan University, Shanghai 200433, China
Taozeng Zhu: Institute of Supply Chain Analytics, Dongbei University of Finance and Economics, Dalian 116025, China
Operations Research, 2025, vol. 73, issue 5, 2708-2728
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.
Keywords: Optimization; robust optimization; robust satisficing; predictive analytics; prescriptive analytics (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:73:y:2025:i:5:p:2708-2728
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