drone-assisted parcel delivery to remote islands is increasingly replacing traditional methods, offering improved efficiency and enhanced service reliability. This paper addresses the drone scheduling problem in islan...
详细信息
drone-assisted parcel delivery to remote islands is increasingly replacing traditional methods, offering improved efficiency and enhanced service reliability. This paper addresses the drone scheduling problem in island delivery (DSP-ID) by optimising drone delivery routes. In particular, we first introduce a bi-objective mixed-integer linear programming model that concurrently optimises delivery time and energy consumption. To address the model, both a heuristic non-dominated sorting genetic algorithm II (NSGA-II) and an exact augmented epsilon-constraint method are developed. The efficacy and robustness of the proposed model and algorithms are evaluated through experiments across various scales. Results indicate that both algorithms yield high-quality solutions for DSP-ID in small-scale scenarios. However, as the problem size expands, the performance of the augmented epsilon-constraint method wanes under time constraints, whereas the NSGA-II consistently delivers high-quality solutions. Additionally, we provide decision-makers with actionable insights for selecting the most effective drone delivery routes.
Amid growing interest in the integration of drones into maritime logistics, this paper addresses the drone scheduling problem in shore-to-ship delivery (DSP-SSD), which is both significant and challenging. We introduc...
详细信息
Amid growing interest in the integration of drones into maritime logistics, this paper addresses the drone scheduling problem in shore-to-ship delivery (DSP-SSD), which is both significant and challenging. We introduce a mixed-integer programming model with time discretization that incorporates drone-related constraints, moving targets, and the need for multiple drone trips. While commercial solvers can handle this model in small-scale scenarios, we propose a tailored branch-and-price-and-cut (BPC) algorithm for larger and more complex cases. This algorithm integrates a drone-specific backward labeling algorithm, cutting planes, and acceleration methods to boost its effectiveness. Experiments show that the BPC algorithm substantially outperforms the commercial solvers in terms of solution quality and computational efficiency and that the inclusion of acceleration strategies in the algorithm enhances its performance. We also provide detailed sensitivity analyses of critical parameters of the model, such as the time discretization parameter and the number of ships, to gain insights into how our approach could be applied in real-world DSP-SSD operations.
How to effectively organize drones to monitor pollutants from vessels is an important operational problem in port management. It is defined as the drone scheduling problem (DSP). The effectiveness of precise algorithm...
详细信息
How to effectively organize drones to monitor pollutants from vessels is an important operational problem in port management. It is defined as the drone scheduling problem (DSP). The effectiveness of precise algorithms and heuristic algorithms in solving DSP has been reported in previous studies. In previous studies, the speed of the vessel was assumed to be constant. However, since the influence of sea waves and vessel power, such an assumption is difficult to satisfy in actual scenarios. The actual position of the vessel may deviate from the position information obtained through prior calculations. As the cumulative position deviation increases, it is possible to make the original feasible monitoring scheme infeasible. It is necessary to consider the emission monitoring dispatching of drones under vessel speed fluctuation in actual monitoring activities of the vessel. To deal with the problem, a dynamic dispatching strategy based on reinforcement learning (RL) is proposed. Considering the vessel speed fluctuation, the monitoring window is divided into multiple sub-time windows. The route information of the vessel in each sub-time window is updated according to the vessel speed fluctuations to reduce the accumulation of deviations between the prior position and the actual position. Then, a lightweight RL strategy is adopted to quickly (re)organize the monitoring scheme in each sub-time window. Numerical experiments illustrate the above division-conquer approach could effectively reduce the possibility of drone monitoring failure caused by vessel speed fluctuations. Also, the superiority of the RL-based dispatching strategy is illustrated by comparing it with multiple dispatching schemes.
暂无评论