Optimizing decision-making with integrated possibility, necessity, and credibility measures in hesitant fuzzy linear programming under uncertainty with industrial application
提出一种在犹豫模糊线性规划中集成可能性、必要性和可信度测度的方法，通过机会约束框架处理不确定性和犹豫性，提升决策质量，适用于工业等场景。
This paper presents a novel approach to solving hesitant fuzzy linear programming (HFLP) problems by leveraging possibility, necessity and credibility measures within a chance-constrained framework. Hesitant fuzzy sets (HFS) allow for the representation of uncertainty where multiple possible values for membership grades exist offering a more nuanced model for real-world problems characterized by ambiguity and partial truth. In our proposed method we integrate possibility and necessity measures to handle the inherent fuzziness and hesitancy in the data thus providing a robust mechanism to evaluate the feasibility and reliability of solutions. The chance-constrained programming technique is employed to ensure that constraints are satisfied to a specified confidence level accommodating the stochastic nature of the problem parameters. This integration enhances the decision-making process by accounting for both the likelihood and the necessity of events, leading to more informed and reliable outcomes. We demonstrate the efficacy of our approach through Industrial application and showcasing its potential along in as applications where uncertainty and hesitancy are prevalent. The results indicate that our method not only improves solution quality but also offers flexibility in adjusting the levels of optimism and pessimism in decision-making. This work contributes to the advancement of HFLP by providing a comprehensive framework that bridges fuzzy uncertainty with probabilistic constraint satisfaction.