Distance-based methods for multi-objective optimization problems: Fuzzy and non-fuzzy approaches

Authors:
  • PGPinki Gulia
    Pinki Gulia
  • RKRakesh Kumar
    Rakesh Kumar
Open Access
Publisher website
Google Scholar
Abstract
Due to the general inherent volatility of the data, it is difficult to draw firm conclusions about it in real time. Like in decision-making situations, an optimization problem known as a vector-maximum problem includes several objective functions. It is explained here how to deal with the fuzzy multi-objective optimization linear programming problem (FMOLPP) by utilizing several fuzzy and non-fuzzy ways. Reducing the gap between ideal and feasible goals is at the heart of the suggested technique. Using ranking functions, problems that are ambiguous can be made to be more precise and precise. The employment of precise optimization techniques has become standard. The very first model handles MOLPP by solving a sequence of single-goal sub-problems where the objectives are turned into constraints. Using an ambiguous strategy, the fuzzy version of the perpendicular divergence (between the parallel hyperplanes at the ideal locations of the desired function) is possible. Supreme perpendicular separation has developed a suitable degree of membership. To better understand these hypotheses, numerical experiments had to be looked at.

This publication has 22 references indexed in Scilit: