Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

Uncertainty in input data significantly affects the quality of solutions in mathematical optimization, making it crucial to explore alternative decisions when the selected decision becomes unavailable or suboptimal. This study introduces a novel risk-averse decision-making approach for cases where objective function coefficients are uncertain in multi-objective combinatorial optimization problems. We construct a region in the objective space based on reference solutions obtained from the deterministic formulation. Alternative decisions, identified using a neighboring structure that falls within this region, are used to determine risk-preference solutions. We propose two sets of indices to quantify the quality of outcomes and neighboring decisions in terms of performance and risk level. The approach is demonstrated in diverse test cases, highlighting its effectiveness in improving risk-averse decision-making under uncertainty.

Keywords: Multi-objective Combinatorial Optimization, Neighboring Decisions, Risk-averse Decision-Making, Sensitivity Region, Uncertainty.

Document Type

posters

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by/4.0/

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Risk-Averse Decision-Making in Multiobjective Combinatorial Optimization

Uncertainty in input data significantly affects the quality of solutions in mathematical optimization, making it crucial to explore alternative decisions when the selected decision becomes unavailable or suboptimal. This study introduces a novel risk-averse decision-making approach for cases where objective function coefficients are uncertain in multi-objective combinatorial optimization problems. We construct a region in the objective space based on reference solutions obtained from the deterministic formulation. Alternative decisions, identified using a neighboring structure that falls within this region, are used to determine risk-preference solutions. We propose two sets of indices to quantify the quality of outcomes and neighboring decisions in terms of performance and risk level. The approach is demonstrated in diverse test cases, highlighting its effectiveness in improving risk-averse decision-making under uncertainty.

Keywords: Multi-objective Combinatorial Optimization, Neighboring Decisions, Risk-averse Decision-Making, Sensitivity Region, Uncertainty.