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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Abstract	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.
AbstractList	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.
Author	Ma, Jing Qin, Yong Wang, Xindi
Author_xml	– sequence: 1 givenname: Xindi orcidid: 0000-0002-5987-2546 surname: Wang fullname: Wang, Xindi email: 230228703@seu.edu.cn organization: School of Economics and Management, Southeast University, Nanjing, China – sequence: 2 givenname: Jing orcidid: 0009-0009-2807-2421 surname: Ma fullname: Ma, Jing organization: School of Economics and Management, Southeast University, Nanjing, China – sequence: 3 givenname: Yong surname: Qin fullname: Qin, Yong organization: Business School, Sichuan University, Chengdu, China
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SubjectTerms	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
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Title	A Distributionally Robust Bi-Level Multi-Objective Decision Making Method Under Hybrid Uncertainty
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