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The aim of this paper is to develop an extended linear assignment method to solve multi-criteria decision-making (MCDM) problems under the Pythagorean fuzzy environment, where the criteria values take the form of the Pythagorean fuzzy numbers (PFNs) and the information about criteria weights are correlative. In order to obtain the criteria-wise rankings of the linear assignment method, we firstly define a new likelihood for the comparison between PFNs. Then, we introduce the fuzzy measure to determine the weighted-rank frequency matrix of the linear assignment method. Unlike the existing literature of the fuzzy measure, this paper incorporates the partitioned structure of the criteria set into it and proposes a new partitioned fuzzy measure. Further, we design the extended linear assignment method by using the new likelihood of PFNs and partitioned fuzzy measure for Pythagorean fuzzy multi-criteria decision-making (PFMCDM). Finally, a practical example is used to illustrate and verify our proposed method. [ABSTRACT FROM AUTHOR]

In the real decision making, q‐rung orthopair fuzzy sets (q‐ROFSs) as a novel effective tool can depict and handle uncertain information in a broader perspective. Considering the interrelationships among the criteria, this paper extends Choquet integral to the q‐rung orthopair fuzzy environment and further investigates its application in multicriteria two‐sided matching decision making. To determine the fuzzy measures used in Choquet integral, we first define a pair of q‐rung orthopair fuzzy entropy and cross‐entropy. Then, by utilizing λ‐fuzzy measure theory, we propose an entropy‐based method to calculate the fuzzy measures upon criteria. Furthermore, we discuss q‐rung orthopair fuzzy Choquet integral operator and its properties. Thus, with the aid of q‐rung orthopair fuzzy Choquet integral, we consider the preference heterogeneity of the matching subjects and further explore the corresponding generalized model and approach for the two‐sided matching. Finally, a simulated example of loan market matching is given to illustrate the validity and applicability of our proposed approach. [ABSTRACT FROM AUTHOR]