arXiv:2607. 05683v1 Announce Type: new Abstract: Battery charging of Autonomous Mobile Robots (AMRs) in warehouses is a critical operational challenge that heavily impacts both order processing times and throughput.
arXiv:2606. 31347v1 Announce Type: new Abstract: The electrification of transportation through electric vehicles introduces new challenges for power grid management, such as increased peak demand, voltage fluctuations, line overloads, and the integration of variable renewable energy sources.
arXiv:2606. 06201v1 Announce Type: new Abstract: Pharmaceutical supply chains (PSCs) struggle with inventory management (IM) due to unpredictable demand patterns and variable lead times associated with restocking.
arXiv:2510. 21127v2 Announce Type: replace-cross Abstract: Despite rapid advancements in sensor networks, conventional battery-powered sensor networks suffer from limited operational lifespans and frequent maintenance requirements that severely constrain their deployment in remote and inaccessible environments.
arXiv:2605. 13221v2 Announce Type: replace Abstract: In cloud manufacturing, unmanned aerial vehicles (UAVs) can support both product collection and mobile edge computing (MEC).
arXiv:2606. 02049v1 Announce Type: new Abstract: The increasing integration of renewable energy sources into power systems, particularly in buildings equipped with photovoltaic (PV) panels and energy storage systems, introduces significant complexity in energy systems.
The paper introduces a novel end‑to‑end, size‑agnostic graph reinforcement learning framework for the one‑dimensional bin packing problem (1D‑BPP). It models packing as a Markov decision process on an item‑compatibility graph, where a graph neural network actor‑critic policy learns to merge compatible partial bins. Empirical results on the BPPLIB benchmark show that the learned policy reduces the mean optimality gap of a constructive heuristic from 2.66 % to 2.31 %, performs competitively against other learned methods, and outperforms a state‑of‑the‑art learned solver on the hardest benchmark family.
OpenHail is an open-source Gymnasium environment designed for controlling electric ride‑hailing fleets. It offers a fixed‑size observation–action interface that handles request assignment, repositioning, and charging, while its event‑driven simulator models pickup deadlines, vehicle job queues, battery dynamics, and finite‑capacity charging facilities with FIFO queues. The environment supports various decision‑epoch mechanisms—event‑driven, periodic, hybrid, and policy‑requested—allowing flexible policy interactions within a unified operational model, and includes tools for evaluation, metrics, and baseline policies.
arXiv:2609.07689v1 Announce Type: new Abstract: In electric delivery fleets, mid-shift charging is non-trivial: each vehicle must decide when, where and how much to charge to finish on time with batt...
arXiv:2606. 19199v1 Announce Type: cross Abstract: The recent growth of EV adoption poses challenges for power systems, including increased peak demand and potential grid instability.
arXiv:2606. 23978v1 Announce Type: cross Abstract: We present an offline reinforcement learning (RL) framework for optimizing SLAM throughput control in a warehouse fulfillment environment.
arXiv:2608. 16435v1 Announce Type: new Abstract: In collection operations, accumulating payload progressively slows the vehicle, imposing a cumulative penalty on routing efficiency.
arXiv:2607. 22356v1 Announce Type: new Abstract: In recent years, the growing complexity of last-mile pickup operations has increased the need for fast and accurate decision-making on logistics platforms.