A Logistics Supplier Combination Decision Method Based on Hesitation Fuzzy BWM-TOPSIS

In the process of selecting logistics suppliers, a single logistics form is increasingly insufficient to meet the demands, necessitating the consideration of various forms of logistics in combination. To address the decision-making problem of logistics supplier combinations in uncertain environments, this paper proposes a robust decision-making method based on hesitant fuzzy BWM-TOPSIS. Firstly, experts utilize interval hesitant fuzzy values to construct a decision matrix. Subsequently, based on the Best Worst Method (Best Worst Method, BWM), the weights of attributes are determined, and the scores of solution attributes are calculated through an interval hesitant fuzzy aggregation operator. Then, drawing on the principles of the Technique for Order Preference by Similarity to an Ideal Solution (Technique for Order Preference by Similarity to an Ideal Solution, TOPSIS), the distances between each equipment and the positive and negative ideal solutions are computed as inputs for the multi-objective optimization model. Finally, a robust optimization method is employed to solve the linear multi-objective programming problem. A case study is conducted on the logistics supplier combination decision problem of a certain enterprise, validating the feasibility and effectiveness of the proposed method.