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.

这项研究提出了一种基于无人机的交付计划方法，该方法考虑了无人机故障，以最大程度地减少预期的需求损失（ELOD）。开发了优化模型（DDS-F），以确定将每架无人机分配给客户的子集以及相应的交付顺序。由于求解优化模型在计算上具有挑战性，因此开发了模拟退火（SA）启发式算法来减少计算时间。拟议的SA具有基于Petal算法的快速初始解决方案生成，用于路径选择的二进制整数规划模型以及用于寻找更好解决方案的局部邻域搜索算法。数值结果表明，该方法在测试用例上将ELOD降低了23.6％，胜过了著名的Makespan问题。进行了几个案例研究，以说明故障分布函数对最佳航班时刻表的影响。此外，所提出的方法能够为本文研究的测试案例获得确切的解决方案。数值结果还表明，与精确算法相比，该算法平均可减少44.35％的计算时间。