Fuzzy Transportation Optimization using Minimum Spanning Tree and Intuitionistic Fuzzy Ranking

Abstract

This paper presents a novel approach for solving transportation problems involving trapezoidal intuitionistic fuzzy numbers (TIFNs) using the Minimum Spanning Tree (MST) procedure, along with value and ambiguity index ranking. The proposed method defuzzifies TIFNs to obtain crisp numbers, facilitating the determination of the optimal transportation plan via the MST procedure and value and ambiguity ranking. By accounting for the fuzzy nature of transportation costs, demands, and supplies, this approach provides a more realistic and robust solution. A numerical example demonstrates the effectiveness of the proposed method, showcasing its ability to efficiently handle fuzzy transportation problems and minimize total transportation cost. This research contributes to the development of fuzzy optimization techniques in transportation planning, offering a practical solution for decision-makers in logistics and supply chain management.