Inferring linear feasible regions using inverse optimization

Kimia Ghobadi and Houra Mahmoudzadeh

European Journal of Operational Research, 2021, vol. 290, issue 3, 829-843

Abstract: Consider a problem where a set of feasible observations are provided by an expert, and a cost function exists that characterizes which of the observations dominate the others and are hence, preferred. Assume the expert has an implicit optimization model in mind to identify the feasible observations, but the explicit constraints of this underlying model are unknown. Our goal is to infer the feasible region of such an optimization model that would render these observations feasible while making the best ones optimal for the cost (objective) function. Such feasible regions (i) build a baseline for a systematic categorization of future observations, and (ii) allow for using sensitivity analysis to discern changes in optimal solutions if the objective function changes in the future.

Keywords: Linear programming; Inverse optimization; Feasible region inference; Loss function; Diet recommendation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:290:y:2021:i:3:p:829-843

DOI: 10.1016/j.ejor.2020.08.048

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