The MCDM Approach Using Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) for Intuitionistic Fuzzy Sets with AHP-Based Weight Information

Abstract Effective municipal solid waste management is currently facing a global problem. The increasing accumulation of these waste contents, resulting in improper waste management, has led to various environmental issues, including frequent greenhouse gas emissions and a lack of space for garbage disposal. Solid waste management is a serious problem in the current era. For this type of problematic issue, the concept of intuitionistic fuzzy sets (IFS) is a promising tool for assessing uncertain and fuzzy information. In this article, we propose the preference ranking organization method for enrichment evaluation (PROMETHEE) in combination with the analytical hierarchy process (AHP). The AHP is used for calculating weight vectors of attributes using IFS information. We present the multi-criteria decision-making (MCDM) algorithm based on AHP to investigate the weightage of attributes and the PROMETHEE approach for ranking the alternatives. We solve the real-life numerical example of finding the best solid waste management company for utilizing solid waste material. To investigate the applicability of the proposed theory, we comprehensively compare it with other fuzzy data aggregation models, such as intuitionistic fuzzy Hamacher weighted averaging and intuitionistic fuzzy Dombi weighted averaging operators. On the other hand, many MCDM approaches based on simple fuzzy sets fail to investigate IFS-based information. Hence, our proposed model offers more computational efficiency and is a suitable tool for investigating complicated MCDM problems.