Disaster relief supply chain network planning under uncertainty

Supply chain planning during disasters can be challenging due to uncertainty in demand and travel time, leading to limited stocks and delivery delays. While previous studies have focused on network planning for disaster relief supply chains under uncertainty, they have not fully integrated all network components while considering various potential factors. This integration is crucial for successful humanitarian relief operations. To address this issue, we propose a comprehensive model using a two-stage mixed-integer stochastic linear programming. The model incorporates facility location, pre-positioning, direct allocation, and multi-depot vehicle routing under demand and travel time uncertainties while examining multi-echelon, multi-commodity, response deadlines, and deprivation costs. We also create an improved random forest algorithm to enhance the accuracy of demand and travel time forecasts. To obtain accurate information for effective decision-making, we develop a data-driven, exact algorithm by combining an improved random forest algorithm and Benders decomposition. Computational experiments show that our proposed algorithm outperforms the L-shaped method in finding a better solution with less running time. We provide a real case to validate our model and algorithms. Our model and solution scheme can help improve efficiency and timeliness while minimizing deficiencies in disaster relief efforts.