An Optimization Approach to Minimize the Expected Loss of Demand Considering Drone Failures in Drone Delivery Scheduling

Abstract

This study proposes a drone-based delivery schedul- ing method considering drone failures to minimize the expected loss of demand (ELOD). An optimization model (DDS-F) is developed to determine the assignment of each drone to a subset of customers and the corresponding delivery sequence. Because solving the optimization model is computationally challenging, a Simulated Annealing (SA) heuristic algorithm is developed to reduce the computational time. The proposed SA features a fast initial solution generation based on the Petal algorithm, a binary integer programming model for path selection, and a local neigh- borhood search algorithm to find better solutions. Numerical results showed that the proposed approach outperformed the well-known Makespan problem in reducing the ELOD by 23.6% on a test case. Several case studies are conducted to illustrate the impact of the failure distribution function on the optimal flight schedules. Furthermore, the proposed approach was able to obtain the exact solutions for the test cases studied in this paper. Numerical results also showed the efficiency of the proposed algorithm in reducing the computational time by 44.35%, on average, compared with the exact algorithm.