STATE-OF-THE-ART SOLUTIONS TO THE FUZZY LINEAR PROGRAMMING PROBLEM
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Abstract
This article outlines the state-of-the-art solutions for solving fuzzy linear programming problems. It begins by explaining the role of fuzzy linear programming in decision-making under uncertain circumstances. The article then dives into various modern solutions such as the Fuzzy Simplex Method, Heuristics Algorithms, Genetic Algorithms, Fuzzy Number Ranking Methods, Fuzzy Goal Programming, and the use of Artificial Neural Networks (ANNs) and Swarm Intelligence (SI). These solutions have evolved to effectively cope with the complexities of fuzzy linear programming, broadening its application in various fields. The article concludes by forecasting the future of fuzzy linear programming resolution techniques, with advancements in artificial intelligence, machine learning, and computational power pointed out as having the potential to create more sophisticated systems.
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