A Distributionally Robust Bi-Level Multi-Objective Decision Making Method Under Hybrid Uncertainty

This paper introduces a novel bi-level multi-objective optimization framework designed for decision-making under hybrid uncertainty, where system constraints account for both intuitionistic fuzzy sets and stochastic uncertainties. By combining interval programming and chance-constrained optimization...

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Veröffentlicht in:IEEE access Jg. 13; S. 155399 - 155410
Hauptverfasser: Wang, Xindi, Ma, Jing, Qin, Yong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
bi-level decision making
chance constraint
convex optimization
Decision making
Decision support systems
Fuzzy sets
Fuzzy-stochastic hybrid programming
Indexes
Multiple objective analysis
Optimization
Probabilistic logic
Probability distribution
Programming
Random variables
Robustness (mathematics)
Stochastic processes
Uncertainty
Water resources
ISSN:2169-3536, 2169-3536
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Zusammenfassung:This paper introduces a novel bi-level multi-objective optimization framework designed for decision-making under hybrid uncertainty, where system constraints account for both intuitionistic fuzzy sets and stochastic uncertainties. By combining interval programming and chance-constrained optimization, the proposed method relaxes the commonly adopted assumption of Gaussian distributions for stochastic uncertainties, thereby making it applicable to a wider range of distributional forms, including non-Gaussian scenarios. To effectively handle the interplay between fuzziness and stochasticity, we propose an acceptability index that quantifies uncertainty propagation, ensuring robust solutions that balance both sources of uncertainty. The approach provides a computationally efficient and theoretically sound decision-support tool for complex bi-level optimization problems. Numerical case studies demonstrate its ability to generate high-confidence solutions while offering flexibility in preference modeling.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3605948