Developing solution algorithm for LR-type fully interval-valued intuitionistic fuzzy linear programming problems using lexicographic-ranking method

TL;DR

This paper introduces LR-type interval-valued intuitionistic fuzzy numbers, develops a solution algorithm for fuzzy linear programming problems using lexicographic ranking, and demonstrates its application in production planning.

Contribution

It presents a new concept of LR-type IVIFNs, establishes their arithmetic and ranking, and formulates an algorithm for solving fuzzy linear programming problems with these numbers.

Findings

The proposed algorithm finds a unique optimal solution.

The method effectively handles fuzzy parameters in linear programming.

Application in production planning demonstrates practical utility.

Abstract

In this article, a new concept of LR-type interval-valued intuitionistic fuzzy numbers (LR-type IVIFN) has been introduced. The theory has also been enriched by demonstrating diagrammatic representations of LR-type IVIFNs and establishing arithmetic operations among these fuzzy numbers. The total order properties of lexicographic criteria have been used for ranking LR-type IVIFNs. Further, a linear programming problem having both equality as well as inequality type constraints with all the parameters as LR-type IVIFNs and unrestricted decision variables has been formulated. An algorithm to find a unique optimal solution to the problem using the lexicographic ranking method has been developed. In the proposed methodology, the given linear programming problem is converted to an equivalent mixed 0-1 lexicographic non-linear programming problem. Various theorems have been proved to show the equivalence of the proposed problem and its different constructions. The model formulation, algorithm and discussed results have not only developed a new idea but also generalized various well-known related works existing in the literature. A numerical problem has also been exemplified to show the steps involved in the approach. Finally, a practical application in production planning is framed, solved and analyzed to establish the applicability of the study.