Author: Satyapal Kumar
Abstract: This paper develops a theoretical framework for integrating fuzzy optimization with Multi-Criteria Decision-Making (MCDM) techniques. Classical optimization assumes precise and deterministic parameters, which rarely exist in real-world decision environments. Fuzzy set theory, introduced by Zadeh in 1965[41], enables the modeling of vagueness and linguistic imprecision. By embedding fuzzy concepts into established MCDM methods such as Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Elimination and Choice Expressing Reality (ELECTRE), decision-makers can address ambiguity and uncertainty effectively. The paper explores fuzzy extensions of AHP and TOPSIS, outlines membership functions (triangular, trapezoidal), and introduces fuzzy goal programming. Research gaps are highlighted, including limited hybridization with artificial intelligence techniques and inadequate large-scale validations. The proposed framework contributes to the theory of decision sciences by providing a systematic pathway for hybrid fuzzy optimization models applicable to diverse fields.
Keywords: Fuzzy Optimization, Multi-Criteria Decision Making, AHP, TOPSIS, ELECTRE, Membership Functions
Page No: 173-188