Possibilistic index-based multi-criteria decision-making with an unknown weight of air pollution model under bipolar fuzzy environment

Nowadays, people face various societal challenges in their daily lives related to air pollution. With many causes of human fastest lifestyle, different metro cities of India are polluted daily. Air pollution is the most significant societal problem in metropolitan cities in India. People have various health issues due to the air pollution problem in the metro cities. Kolkata is not an exception because Kolkata is India’s second most polluted metro city. With decision-making approaches, we can reduce air pollution in a city. So making the proper decision is the primary focus of reducing air pollution problems. We can easily discover the most affected causes of air pollution in this city through the decision-making approaches. Therefore, we have developed a novel multi-criteria decision-making (MCDM) technique and applied it to the air pollution model in Kolkata city. The main contribution of this research is that we invented a possibilistic ranking index of bipolar fuzzy (BpF) numbers based on the possibilistic mean and variance and applied it to the air pollution model. Using the possibilistic ranking index determined the criteria weight of air pollution MCDM model under BpF environment. Due to many uncertainty and negative aspects, the alternative information BpF numbers consider the air pollution MCDM model. The BpF numbers deal with both positive and negative aspects of the causes of pollution in the air pollution MCDM model. By BpF environment, the possibilistic index ranking method efficiently solves the air pollution MCDM model. Finally, a numerical illustration has been presented to demonstrate the applicability and feasibility of our suggested approach.