Multi-attribute decision-making approach based on Choquet integral under probabilistic dual hesitant fuzzy information and its application to photovoltaic cells

Driven by the global trend towards environmental protection, tackling the multi-attribute decision-making (MADM) process for photovoltaic cells has become increasingly critical. However, MADM is inherently characterized by uncertainty and fuzziness. The probabilistic dual hesitant fuzzy set (PDHFS), as an advanced extension of fuzzy sets, exhibits enhanced flexibility and superiority in characterizing decision information within the framework of MADM. To more efficiently integrate fuzzy information, we explore MADM problems through the lens of the Choquet integral. Initially, we introduce two novel operators called the probabilistic dual hesitant fuzzy Choquet ordered average (PDHFCOA) operator and the probabilistic dual hesitant fuzzy Choquet ordered geometric (PDHFCOG) operator. Upon verification, these operators are found to possess numerous advantageous properties. Subsequently, we extend these two operators to their generalized counterparts, namely the generalized probabilistic dual hesitant fuzzy Choquet ordered average (GPDHFCOA) operator and the generalized probabilistic dual hesitant fuzzy Choquet ordered geometric (GPDHFCOG) operator. This extension allows for a more comprehensive aggregation of PDHF information. Ultimately, we propose a novel MADM approach for the selection problem of photovoltaic cells. Comparing our method with four existing aggregation operators and two decision-making approaches, the results demonstrate the effectiveness and feasibility of the proposed technique.