Line-haul trucking costs are dominated by three comparably sized components: energy, driver labor, and equipment. Most efficiency technologies address only one component at a time.
arXiv:2604. 09938v2 Announce Type: replace-cross Abstract: Conventional field operations spend most of their energy moving the tractor body, not the implement.
SHIFT‑Truck is the first high‑fidelity aerodynamics dataset and benchmark specifically for pickup trucks, comprising 1,000 Spalart‑Allmaras delayed detached‑eddy simulations of a reference truck geometry morphed across 17 shape parameters. Each simulation uses a ~100‑million‑cell mesh at a Reynolds number of 1.4×10⁷ and provides time‑averaged surface pressure, wall shear stress, volumetric pressure, and velocity data, verified by grid refinement, repeated runs, and wind‑tunnel measurements. The benchmark defines geometry‑grouped splits and evaluates four neural surrogates—DoMINO, GeoTransolver, AB‑UPT, and SMART—on surface and volume tracks, while also introducing controlled distribution shifts in operating point, surface discretization, and vehicle archetype to test generalization.
arXiv:2606. 30680v1 Announce Type: cross Abstract: Truck-drone delivery is an emerging last-mile logistics mode combining the long-haul capacity of trucks with the flexible service capability of drones.
EVTradeMatch is a mobility-aware, multi-objective matching framework that coordinates peer-to-peer energy trading between electric vehicles (EVs). It uses a prediction-guided score for charging-node suitability and formulates the matching problem as a mixed-integer linear program, solved via a tailored NSGA-II algorithm. Experiments show significant gains in transferred energy, charging-node suitability, and matching coverage compared to existing proximity- and auction-based methods.
arXiv:2606. 16051v1 Announce Type: cross Abstract: Residential battery energy storage systems (BESS) are increasingly deployed alongside photovoltaic (PV) generation to reduce household energy costs under volatile time-of-use (TOU) tariffs.
arXiv:2607. 07343v1 Announce Type: cross Abstract: Online truckload bid acceptance is a closed-loop stochastic decision problem in which a carrier or broker must, in real time, accept or reject a tendered load subject to operational feasibility, fleet repositioning costs, and opportunity cost against future demand.
arXiv:2606. 26400v1 Announce Type: new Abstract: Agentic systems are changing how complex operational tasks are coordinated, introducing a new paradigm for connecting heterogeneous data sources and automating processes.
The paper introduces an integrated optimization framework that links automated warehouse operations with last‑mile multi‑modal transport for differentiated on‑demand delivery. It employs a deep reinforcement learning approach—MORM‑AGDQN for warehouse scheduling and MRMH‑HCVRP for external routing—to balance service level, cost, and demand. The results demonstrate significant performance gains, including a 100 % on‑time delivery rate, a 29.3 % reduction in average last‑mile delivery time, a 46.4 % cut in total transportation distance, and a high‑priority service rate exceeding 92 % while maintaining cost‑customer satisfaction balance.
The paper presents a conditional diffusion model that generates electric vehicle battery‑current profiles conditioned on route features such as velocity and ambient temperature. Using a latent conditioning encoder and a temporal 1D U‑Net denoising backbone, the model produces realistic current trajectories that capture both the overall envelope and sharp transient events. On a dataset of 12,000 trips from nine vehicles, the model achieves a Wasserstein distance of 0.0029, outperforming direct condition injection by 89.1% in Wasserstein distance and 52.8% in MAE.
arXiv:2607. 06066v1 Announce Type: new Abstract: The Vehicle Routing Problem (VRP) and its variants represent some of the most practically consequential optimization challenges in modern logistics and urban mobility.
arXiv:2608. 14974v1 Announce Type: new Abstract: This paper presents a demand-driven framework for on-demand Urban Air Mobility (UAM) network design that links vertiport siting, fleet simulation, and door-to-door travel-time feasibility.
This study evaluates how flexible electric vehicle charging can improve energy efficiency and grid stability. Using real‑world data from public charging stations in Prague, the authors analyze individual and aggregated charging sessions to show that optimizing charging times reduces energy waste and grid imbalances. By aligning EV charging with periods of lower demand and higher renewable generation, they demonstrate a significant improvement in energy efficiency and a reduced need for costly system support.