Robust Satisficing

Daniel Zhuoyu Long (), Melvyn Sim () and Minglong Zhou ()
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Daniel Zhuoyu Long: Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, China
Melvyn Sim: Department of Analytics and Operations (DAO), NUS Business School, National University of Singapore, Singapore 119245
Minglong Zhou: Department of Management Science, School of Management, Fudan University, Shanghai 200437, China

Operations Research, 2023, vol. 71, issue 1, 61-82

Abstract: We present a general framework for robust satisficing that favors solutions for which a risk-aware objective function would best attain an acceptable target even when the actual probability distribution deviates from the empirical distribution. The satisficing decision maker specifies an acceptable target, or loss of optimality compared with the empirical optimization model, as a trade-off for the model’s ability to withstand greater uncertainty. We axiomatize the decision criterion associated with robust satisficing, termed as the fragility measure , and present its representation theorem. Focusing on Wasserstein distance measure, we present tractable robust satisficing models for risk-based linear optimization, combinatorial optimization, and linear optimization problems with recourse. Serendipitously, the insights to the approximation of the linear optimization problems with recourse also provide a recipe for approximating solutions for hard stochastic optimization problems without relatively complete recourse. We perform numerical studies on a portfolio optimization problem and a network lot-sizing problem. We show that the solutions to the robust satisficing models are more effective in improving the out-of-sample performance evaluated on a variety of metrics, hence alleviating the optimizer’s curse.

Keywords: Optimization; robust optimization; robust satisficing; data-driven; discrete optimization; stochastic optimization; fragility measure (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:71:y:2023:i:1:p:61-82

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