Innovative intuitionistic fuzzy fairly aggregation operators with linear programming based decision-making approach

Intuitionistic fuzzy set (InFS) theory represents a paradigm change in handling strategic planning challenges, one of the most important issues in the physical realm. Aggregation operators (AOs) have a big part to play in making decisions, especially when there are many things to think about. When there isn’t enough information, it’s hard to come up with good accretion solutions. This article aims to establish innovative operational rules and AOs in an intuitionistic fuzzy enviroment. To accomplish this aim, we establish novel operational laws that utilize the notion of proportional distribution to provide a neutral or fairly remedy for InFSs. Furthermore, using suggested fairly AOs with evaluations from multiple “decision-makers” (DMs) and partial weight details under InFS, a fairly “multi-criteria decision-makin” (MCDM) method is constructed. A linear programming model is used to figure out the weights of criteria when only some of the information is known. In addition, a rigorous implementation of the proposed method is provided to illustrate the efficacy of the proposed AOs.