2025, Vol. 10, Issue 10, Part A

As traditional transportation models often rely on crisp, deterministic data, which is frequently unavailable or unrealistic, fuzzy numbers are used instead to model several critical types of uncertainty. Fuzzy numbers are not just widely used but are essential tools for making transportation and logistics models applicable to real-world scenarios. By explicitly quantifying uncertainty in supply, demand, cost, and time, they enable planners and managers to develop more robust, flexible, and realistic strategies. In this study, the classical fully fuzzy transportation problem, which is a comprehensive fuzzy extension of the transportation model, has been extended to a fully intuitionistic fuzzy transportation problem to better handle imprecise and hesitant information in real-world logistics scenarios. The model is formulated by representing transportation costs, supply, and demand parameters as Triangular Intuitionistic Fuzzy Numbers (TIFNs), which offer a more comprehensive framework by incorporating both membership and non-membership degrees. Furthermore, a new method has been developed to derive the initial basic feasible solution and to obtain an optimal solution. The proposed method enhances the process of decision-making under uncertainty by capturing both ambiguity and hesitation inherent in transportation data. A detailed numerical example is introduced to illustrate the applicability and effectiveness of the proposed method.