Paper Detail
Kabir Murjani, Abhay Sobhanan
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.
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@misc{murjani2026drive,
title = {Drive, Pack, Fly: The Travelling Thief Problem with Drone},
author = {Kabir Murjani and Abhay Sobhanan},
year = {2026},
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 Dro},
url = {https://huggingface.co/papers/2608.16435},
keywords = {Travelling Thief Problem with Drone, mixed-integer linear program, metaheuristics, attention-based Deep Reinforcement Learning, DRL policy, learner-initialised hybrid solver, annealing, code available, huggingface daily},
eprint = {2608.16435},
archiveprefix = {arXiv},
}
{}