考虑软时间窗的多趟无人机取货问题

IF 7.6 1区 工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY
Shanshan Meng , Yanru Chen , Dong Li
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引用次数: 0

摘要

我们将取货和送货问题扩展到卡车-无人机联合操作,假设一组卡车可以停在客户节点,并发射无人机执行多个具有软时间窗口的取货和送货服务。与其他研究过的取货和送货问题不同,本文考虑了即时零售和食品配送中典型的一对一取货和送货服务(其中取货和送货请求具有一对一关系)。我们建立了一个混合整数非线性程序的数学模型,并引入了强化策略来捕获这种情况,目标是最小化总成本,包括违反时间窗的惩罚成本。我们用分段线性方法逼近悬停无人机的非线性功率,并提出了一种有效的元启发式方法,以及卡车等待时间优化,以解决大尺寸问题。最后进行了全面的计算实验,验证了算法的适用性和不同配置的影响。数值结果表明了所提出的模型和求解方法的有效性,并展示了实现组合系统所获得的潜在操作增益。与纯卡车模式相比,成本节约率平均超过40%,并且我们的算法在解决质量方面优于文献中的基准算法10%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pickup and delivery problem with multi-visit drones considering soft time windows
We extend the pickup and delivery problem with combined truck–drone operation by assuming that a fleet of trucks can stop at customer nodes and launch drones to perform multiple pickup and delivery services with soft time windows. Unlike other studied pickup and delivery problems, one-to-one pickup and delivery services (in which the pickup and delivery requests have a one-to-one relationship), which are typical in instant retail and food delivery, are considered. We mathematically model a mixed-integer nonlinear program and introduce strengthening strategies to capture this scenario, with the objective of minimising the total cost, including the penalty cost of time window violations. We approximate the nonlinear power of hovering drones with a piecewise linear method and propose an efficient metaheuristic approach, along with truck waiting time optimisation, to solve large-size problems. Finally, comprehensive computational experiments are conducted, which demonstrate the applicability of the algorithm and the impacts of different configurations. The numerical results indicate the efficiency of our proposed model and the solution approach, demonstrating the potential operational gain obtained by implementing the combined system. The cost savings rate compared to the truck-only mode is more than 40% on average, and our algorithm outperforms the benchmark algorithms in the literature by more than 10% in terms of solution quality.
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来源期刊
CiteScore
15.80
自引率
12.00%
发文量
332
审稿时长
64 days
期刊介绍: Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.
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