Hierarchical Multiobjective Linear Fractional Stochastic Optimization Problem Under Neutrosophic Environment With Its Application in Industrial Production Planning Problems

In real-world organizations—such as industry companies, agriculture sectors, and transportation control systems—there exist several complexities, such as hierarchical decision-makers (DMs) optimizing multiple conflicting ratio-based goals, resources associated with chance, and uncertain input data with a hesitant scope. Thus, the current study focuses on investigating a new method that merges goal programming, stochastic optimization, and intuitionistic fuzzy optimization approaches for solving such hierarchical multiobjective fractional stochastic neutrosophic optimization (HMOFSNO) problems. The proposed framework is designed to effectively handle the upper–lower DM's decision unlock process, uncertainty with hesitation, and Cauchy random constraint quantity that exists in real-life problems due to imprecise, incomplete data, and conflicting goals. In the proposed method, the memberships and nonmemberships are investigated to measure the degree of satisfaction and dissatisfaction for each imprecise goal of DMs. In the existing research study, there is a lack of an effective optimization model that accounts for hesitation, uncertainty, and chance constraints simultaneously in bilevel multiple fractional objective problems, despite its applicability in different real fields. In this paper, such limitations are addressed, and the efficiency of the suggested method is illustrated by solving two numerical examples and a practical industrial production planning problem.