A Symmetric and Comparative Study of Decision Making in Intuitionistic Multi-objective Optimization Environment: Past, Present and Future

Abstract

In this article, we look at how intuitionistic fuzzy programming (IFP) for MOO works in several real-life situations. Problems in the real world frequently have non-linear properties, in contrast to the majority of MOO research, which has traditionally relied on linear assignment functions in an intuitionistic setting. To tackle this, our research takes into account non-linear functions such as hyperbolic, parabolic, exponential, and s-curved functions. These functions handle the constraints caused by convexity and concavity in certain areas of the domain, as well as the impact of the functions' slopes. We then investigate 25 potential hybrid scenarios involving various membership and non-membership functions in IFP methods. Evaluating how these hybrid scenarios affect IFP's ability to handle the complexity of MOO is our main goal. By evaluating how various scenarios perform, we attempt to determine the best setups and comprehend their advantages and disadvantages. The results of our quantitative evaluations and practical implementations shed light on multi-objective optimization in real-world settings, which is useful for practitioners and decision makers. To further illustrate the real-world consequences of different IFP approaches, we offer an engaging case study in the agricultural sector. This study not only consolidates current knowledge but also provides practical assistance for achieving optimal results in diverse situations, enhancing our grasp of optimization strategies based on IFP.