An interactive fuzzy goal programming algorithm to solve decentralized bi-level multiobjective fractional programming problem
This work addresses optimization challenges in hierarchical decision-making systems, but it appears incremental as it builds on existing fuzzy goal programming methods.
The paper tackled decentralized bi-level multiobjective fractional programming problems by proposing a fuzzy goal programming algorithm based on Taylor series to linearize fractional membership functions and find satisfactory solutions for all decision makers, with a numerical example used to illustrate its effectiveness.
This paper proposes a fuzzy goal programming based on Taylor series for solving decentralized bi-level multiobjective fractional programming (DBLMOFP) problem. In the proposed approach, all of the membership functions are associated with the fuzzy goals of each objective at the both levels and also the fractional membership functions are converted to linear functions using the Taylor series approach. Then a fuzzy goal programming is proposed to reach the highest degree of each of the membership goals by taking the most satisfactory solution for all decision makers at the both levels. Finally, a numerical example is presented to illustrate the effectiveness of the proposed approach.