AIHOT 于 2026-08-17 收录了“带无人机的旅行窃贼问题（TTP-D）：联合优化物品选择、车辆路径与飞行同步”这一公开动态。以下先呈现从来源页面抓取的正文，再给出 AIHOT 摘要与 TopoReduce 编辑解读。
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[2608.16435] Drive, Pack, Fly: The Travelling Thief Problem with Drone
Computer Science > Artificial Intelligence
arXiv:2608.16435 (cs)
-
[Submitted on 17 Aug 2026]
Title:Drive, Pack, Fly: The Travelling Thief Problem with Drone
Authors:Kabir Murjani, Abhay Sobhanan
View a PDF of the paper titled Drive, Pack, Fly: The Travelling Thief Problem with Drone, by Kabir Murjani and 1 other authors
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Abstract:In collection operations, accumulating payload progressively slows the vehicle, imposing a cumulative penalty on routing efficiency. An onboard drone can offset this penalty by retrieving outlying items, thereby shortening the makespan and increasing operational profit. However, travel time remains load-dependent, and each item collected by the ground vehicle shifts the arrival times that govern the drone's launch and rendezvous points. This paper introduces the Travelling Thief Problem with Drone (TTP-D), which maximises the collected profit, net of a time-based rental cost, by jointly optimising item selection, vehicle routing, and flight synchronisation. We formulate a mixed-integer linear program that solves small instances to optimality, and develop both metaheuristics and an attention-based Deep Reinforcement Learning (DRL) policy for larger instances. We further propose a learner-initialised hybrid solver, in which the DRL policy constructs an initial solution that a short annealing run subsequently refines. On two benchmark sets, this hybrid recovers most of the metaheuristic baseline's quality at a fraction of its computational budget, although the largest instances still require the baseline at its full budget. Finally, a sensitivity analysis reveals that the rental ratio is the primary driver of profitability, whereas the fleet parameters affect profit only at the margin.
Subjects:
Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Optimization and Control (math.OC)
Cite as:
arXiv:2608.16435 [cs.AI]
(or
arXiv:2608.16435v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.16435
Focus to learn more
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Abhay Sobhanan [view email]
[v1]
Mon, 17 Aug 2026 11:34:15 UTC (182 KB)
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arXiv:2608.16435 (cs)
-
[Submitted on 17 Aug 2026]
Title:Drive, Pack, Fly: The Travelling Thief Problem with Drone
Authors:Kabir Murjani, Abhay Sobhanan
View a PDF of the paper titled Drive, Pack, Fly: The Travelling Thief Problem with Drone, by Kabir Murjani and 1 other authors
View PDF
HTML (experimental)
Abstract:In collection operations, accumulating payload progressively slows the vehicle, imposing a cumulative penalty on routing efficiency. An onboard drone can offset this penalty by retrieving outlying items, thereby shortening the makespan and increasing operational profit. However, travel time remains load-dependent, and each item collected by the ground vehicle shifts the arrival times that govern the drone's launch and rendezvous points. This paper introduces the Travelling Thief Problem with Drone (TTP-D), which maximises the collected profit, net of a time-based rental cost, by jointly optimising item selection, vehicle routing, and flight synchronisation. We formulate a mixed-integer linear program that solves small instances to optimality, and develop both metaheuristics and an attention-based Deep Reinforcement Learning (DRL) policy for larger instances. We further propose a learner-initialised hybrid solver, in which the DRL policy constructs an initial solution that a short annealing run subsequently refines. On two benchmark sets, this hybrid recovers most of the metaheuristic baseline's quality at a fraction of its computational budget, although the largest instances still require the baseline at its full budget. Finally, a sensitivity analysis reveals that the rental ratio is the primary driver of profitability, whereas the fleet parameters affect profit only at the margin.
Subjects:
Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Optimization and Control (math.OC)
Cite as:
arXiv:2608.16435 [cs.AI]
(or
arXiv:2608.16435v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.16435
Focus to learn more
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Abhay Sobhanan [view email]
[v1]
Mon, 17 Aug 2026 11:34:15 UTC (182 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Drive, Pack, Fly: The Travelling Thief Problem with Drone, by Kabir Murjani and 1 other authors
- View PDF
- HTML (experimental)
- TeX Source
view license
Current browse context:
cs.AI
< prev
|
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new
|
recent
| 2026-08
Change to browse by:
cs
cs.NE
math
math.OC
References & Citations
- NASA ADS
- Google Scholar
- Semantic Scholar
export BibTeX citation
Loading...
BibTeX formatted citation
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loading...
Data provided by:
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Bibliographic and Citation Tools
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alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
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DagsHub Toggle
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GotitPub Toggle
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Huggingface Toggle
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ScienceCast (What is ScienceCast?)
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Spaces Toggle
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AIHOT 摘要
论文提出带无人机的旅行窃贼问题（TTP-D），通过联合优化物品选择、车辆路径与飞行同步，最大化扣除时间租赁成本后的收集利润。研究构建了可精确求解小规模实例的混合整数线性规划，并为大规模实例开发了元启发式与基于注意力的深度强化学习（DRL）策略。所提出的学习者初始化混合求解器能以极小计算预算恢复元启发式基线的大部分质量，敏感性分析显示租赁比率是盈利能力的主要驱动因素。
为什么值得关注
在经典旅行窃贼问题上加入无人机并显式建模负载对行驶时间的影响，将取货、路径与飞行同步纳入同一个优化目标，为物流调度研究提供了更贴近实际的问题形式。
工程化解读
从 TopoReduce 的工程视角看，这条信息属于“论文与研究”主题。它的价值不只在于一个新产品或新观点本身，还在于说明 AI 系统正在如何影响模型接入、智能体协作、研发流程、基础设施和团队决策。实际采用前，应结合原文确认版本、适用范围、价格和运行条件。
- 发布时间：2026-08-17；AIHOT 分类：论文与研究。
- AIHOT 标签：
- AIHOT 判断：在经典旅行窃贼问题上加入无人机并显式建模负载对行驶时间的影响，将取货、路径与飞行同步纳入同一个优化目标，为物流调度研究提供了更贴近实际的问题形式。
- AIHOT 评分：40；评分用于站内排序，不等同于独立评测结论。
TopoReduce 编辑观察
当 AI 动态进入真实生产环境，团队需要同时关注能力边界、数据来源、调用成本、权限控制和可回滚性。把单条新闻放回完整工程链路中阅读，比只看标题更有助于判断它是否适合自己的产品和工作流。