Optimization of Disaster Relief Logistics Distribution Using the Fuzzy Transportation Problem Model

The distribution of disaster relief logistics faces significant challenges due to uncertainty in demand, supply constraints, and accessibility constraints in affected areas. The novelty of this study lies in integrating trapezoidal fuzzy numbers to represent uncertainty in disaster logistics, thereby offering a more realistic model than conventional deterministic models. This study proposes developing a fuzzy-logic-based transportation model to optimize logistics resource allocation. The model was applied to a disaster relief distribution scenario with five source locations and five destination points. The model is solved using the Vogel Approximation Method and optimality test using the Simplex Transportation Method. Next, to determine the distribution route that minimizes costs and distance, a simulation was conducted in MATLAB. The results show that the fuzzy transportation problem model produces more efficient distribution solutions than conventional transportation models, which can be used only for certain data. However, this study is limited to single-objective cost minimization using simulated data. Therefore, future research should consider applying multi-objective optimization to minimize both distribution cost and time simultaneously using real-time geospatial data.