A Two-Stage Stochastic Model For Drone Delivery System With Uncertainty In Customer Demands

Drone delivery is a popular logistics method for e-commerce businesses due to its efficiency and convenience, especially for last-mile delivery and emergency situations in areas with poor infrastructure. However, the uncertainty of customer demands can affect transportation costs in the long run, making it vital to design an effective delivery system. To tackle this issue, we propose a two-stage stochastic model that minimizes the sum of fixed and expected operating costs. The first stage minimizes the total cost of the delivery system, including the facilities fixed costs and expected operating costs, while the second stage arranges drones’ routes according to simulated demands to estimate the minimal expected transportation cost and penalty cost. Since this stochastic programming has infinite scenarios, we deploy a sample average approximation method to estimate its bounds. Additionally, we use a heuristic simulation framework to find a satisfactory solution in an acceptable time.