Bipolar Hesitant Fuzzy Soft Aggregation Operators and Their Application in Sustainable Warehouse Management

A sustainable warehouse management system aims to enhance warehouse operations while reducing contamination and resource utilization. Implementation of different policies for a sustainable warehouse management system is not easy because the factors involved are often vague and complex, which makes decision-making difficult. To address these issues, the study proposed a novel technique, bipolar hesitant fuzzy soft information, which enhanced the decision-making framework for a sustainable warehouse management system. This technique deals with the Yager norm and constructs aggregation operators to handle uncertain selections and criteria with various weights and balances unclear and vague data competently. First, we examine the concept of bipolar hesitant fuzzy soft information, its accuracy and scoring functions, and the basic properties underlying their work. Next, we create an overall multiattribute decision-making model using this hybrid framework to assist decision-makers in selecting the best solution for sustainable warehouse management and include the Yager operations into the bipolar hesitant fuzzy soft set; the proposed technique offers a relatively flexible and powerful informative aggregating operation. Then, the reliability and efficiency of the methodology are confirmed by comparing the proposed technique with another methodology, that is, evaluation based on distance from the average solution. It makes the framework particularly useful in sustainable warehouse management. The research contributes to sustainable warehouse decision-making at both theoretical and practical levels and offers comparative research findings along sensitive analysis that verify its efficiency and its viability in the real world.