Linguistic Pythagorean fuzzy CRITIC-EDAS method for multiple-attribute group decision analysis

Industrialization plays a significant role in the development and growth of a country. Pakistan is one of the developing countries which entirely depends upon their industrial steel sector to meet their economic growth. Pakistan Steel Mills Corporation (PSMC) is a Pakistan’s largest steel industry producing tonnes of steel every year but this production of steel has resulted in a rapid increase in industrial solid waste. Several techniques are available for industrial waste management, ranging from traditional methods to advanced technologies. But the selection of most suitable alternative is a complex task owing to numerous strategies available and multiple attributes involved in selection. To resolve this issue, we introduced a dynamic model for selecting best industrial waste management technique utilizing the linguistic Pythagorean fuzzy sets (LPFSs). The concept of LPFSs has proven to be a productive and advantageous approach in the development of decision problems to manage inconsistency in the escalating sophistication of expert systems. The main goal of this study is to introduce a general MAGDM model by integrating CRiteria Importance Through Intercriteria Correlation (CRITIC) method and Evaluation based on Distance from Average Solution (EDAS) method. Firstly, some Hamacher relations and operations are adapted to linguistic Pythagorean fuzzy environment which initiated a variety of Hamacher operators in the context of LPFSs. Meanwhile some fundamental properties of proposed operators are investigated. Secondly, a novel LPFH-CRITIC-EDAS technique is introduced. In the developed framework, the LPF-CRITIC method is used to calculate the criteria weights and the LPF-EDAS technique is utilized to evaluate the rank of alternatives. Furthermore, the flowchart of the proposed model is presented in a systematic way. Moreover, an example of the industrial waste management technique selection for PSMC is addressed to illustrate the feasibility of the proposed technique. Finally, the new framework is compared with existing techniques to demonstrate its strength. The comparative study reveals that the results of the proposed method are more feasible and practical than the existing techniques.