Pythagorean fuzzy combined compromise solution method integrating the cumulative prospect theory and combined weights for cold chain logistics distribution center selection

Abstract

The evaluation and selection of cold chain logistics distribution centers are of vital importance for third‐party logistics companies which want to build green cold chain logistics networks. To select distribution centers, the conflicts among multiple criteria should be considered. The combined compromise solutions (CoCoSo) method can help enterprises make a structural decision; however, in the original CoCoSo method, the evaluation information was expressed by crisp numbers. Nevertheless, in many cases, because of the imprecision and incompleteness of information, it may be more flexible for evaluators to provide imprecise and fuzzy values rather than crisp numbers. In addition, the judgment values are often expressed based on decision‐makers' psychological expectations. The evaluation criteria of alternatives have relevance to some extent, which would influence the evaluation results. Based on these concerns, this study presents a modified CoCoSo method in the Pythagorean fuzzy environment in which evaluators can express psychological expectations on alternatives. To achieve this goal, the cumulative prospect theory is introduced to obtain the Pythagorean fuzzy prospect weights. Then, an objective weight determination method of criteria under the Pythagorean fuzzy environment is proposed to eliminate the influence of homogeneity of criteria. Based on the Pythagorean fuzzy prospect weights and the combined weights, the original CoCoSo method is extended to the Pythagorean fuzzy environment. A case of selection logistics distribution center is investigated to demonstrate the practicality of the proposed method. The advantages of the proposed method are verified by comparative analysis.