A Novel Entropy-TOPSIS Approach for Selecting Sustainable Logistics Centre Location Under Pythagorean Fuzzy Environment

The amount of information in the Pythagorean fuzzy set is determined by its entropy. Entropy offers a new, versatile tool that is especially helpful in intricate multi-criteria problems where ambiguous data and erroneous information are taken into account. One of the main problems in the research of logistics networks is the optimization of the location of sustainable logistics centres in order to reduce the operational expenses of sustainable logistics distribution centres. A real-world case study that includes a multi-criterion sustainable logistics centre location selection problem serves as an illustration of the performance of the hybrid entropy-TOPSIS approach. The proposed method is defined in a Pythagorean fuzzy environment and uses an integrated entropy measure and TOPSIS to rank the alternatives using defined criteria in the decision-making method to achieve reliable and reasonable results. A relative study is shown to discuss the merits of the method and demonstrate how the suggested method’s computation results are comparable to those of various other modern approaches.