无人机定向运动的问题

IF 4.4 2区 工程技术 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Nicola Morandi, Roel Leus, Hande Yaman
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引用次数: 0

摘要

我们扩展了定向问题(OP)的经典问题设置,将多个无人机与卡车合作访问输入节点的子集。我们把这个问题称为多无人机作战(OP- md)。无人机的电池续航能力有限,因此,它们要么可以与卡车一起移动,而不消耗电池的能量,要么由卡车发射到短途飞行中,必须在不同的客户地点开始和结束。无人机每次飞行只服务一名顾客。此外,卡车和无人机必须在着陆地点相互等待。客户奖品最多只能领取一次,要么在卡车上门时领取,要么在无人机送达时领取。与OP类似,在卡车和无人机在给定时间内返回仓库的条件下,我们将收集到的总奖品最大化。我们为OP-mD提供了一个混合整数线性规划公式,并基于该问题的一种新的分解设计了一种定制的分支切断算法。我们使用标准计算设置在一小时内解决了具有多达50个节点的OP-mD实例。最后,我们调整我们的框架来解决文献中密切相关的问题,并将结果计算性能与先前的研究进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Orienteering Problem with Drones
We extend the classical problem setting of the orienteering problem (OP) to incorporate multiple drones that cooperate with a truck to visit a subset of the input nodes. We call this problem the OP with multiple drones (OP-mD). Drones have a limited battery endurance, and thus, they can either move together with the truck at no energy cost for the battery or be launched by the truck onto short flights that must start and end at different customer locations. A drone serves exactly one customer per flight. Moreover, the truck and the drones must wait for each other at the landing locations. A customer prize can be collected at most once, either upon visiting it by the truck or upon serving it by a drone. Similarly to the OP, we maximize the total collected prize under the condition that the truck and the drones return to the depot within a given amount of time. We provide a mixed-integer linear programming formulation for the OP-mD and devise a tailored branch-and-cut algorithm based on a novel decomposition of the problem. We solve instances of the OP-mD with up to 50 nodes within one hour of CPU time with a standard computational setup. Finally, we adapt our framework to solve closely related problems in the literature and compare the resulting computational performance with that of previous studies.
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来源期刊
Transportation Science
Transportation Science 工程技术-运筹学与管理科学
CiteScore
8.30
自引率
10.90%
发文量
111
审稿时长
12 months
期刊介绍: Transportation Science, published quarterly by INFORMS, is the flagship journal of the Transportation Science and Logistics Society of INFORMS. As the foremost scientific journal in the cross-disciplinary operational research field of transportation analysis, Transportation Science publishes high-quality original contributions and surveys on phenomena associated with all modes of transportation, present and prospective, including mainly all levels of planning, design, economic, operational, and social aspects. Transportation Science focuses primarily on fundamental theories, coupled with observational and experimental studies of transportation and logistics phenomena and processes, mathematical models, advanced methodologies and novel applications in transportation and logistics systems analysis, planning and design. The journal covers a broad range of topics that include vehicular and human traffic flow theories, models and their application to traffic operations and management, strategic, tactical, and operational planning of transportation and logistics systems; performance analysis methods and system design and optimization; theories and analysis methods for network and spatial activity interaction, equilibrium and dynamics; economics of transportation system supply and evaluation; methodologies for analysis of transportation user behavior and the demand for transportation and logistics services. Transportation Science is international in scope, with editors from nations around the globe. The editorial board reflects the diverse interdisciplinary interests of the transportation science and logistics community, with members that hold primary affiliations in engineering (civil, industrial, and aeronautical), physics, economics, applied mathematics, and business.
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