arXiv:2511. 07938v3 Announce Type: replace Abstract: Power-logistics scheduling in modern seaports typically follows a predict-then-optimize pipeline.
arXiv:2606. 24955v1 Announce Type: new Abstract: Power forecasting models deployed in real-world energy markets must operate under nonstationary conditions, where data distributions continually evolve due to weather variability, infrastructure upgrades, and changing consumption behaviors.
arXiv:2607. 05609v1 Announce Type: cross Abstract: The Continual Learning (CL) literature has long been driven by the goal of mitigating catastrophic forgetting.
arXiv:2603. 11201v3 Announce Type: replace-cross Abstract: The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams.
The paper introduces proximal residual value functions for two‑timescale decision systems, where a planning layer supplies a continuation‑value function to a real‑time optimizer that allocates resources, with inventory placement as a motivating example. The authors propose an end‑to‑end reinforcement learning method that learns a convex residual added to a strictly convex potential, enabling well‑posed optimization and end‑to‑end differentiation while maintaining an explicit convex objective for real‑time execution. They also provide necessary and sufficient conditions for smooth value functions to produce decisions consistent across planning and execution timescales, and demonstrate a 5.0% reduction in routing and transfer cost in an offline simulation using data from a large e‑commerce retailer.
arXiv:2504. 13822v3 Announce Type: replace-cross Abstract: The emergence of large pre-trained networks has revolutionized the AI field, unlocking new possibilities and achieving unprecedented performance.
arXiv:2606. 08633v1 Announce Type: new Abstract: Long-horizon maritime trajectory prediction is important for shipping management, logistics planning, and maritime risk analysis, yet month-level forecasting remains insufficiently studied.
arXiv:2607. 11349v1 Announce Type: cross Abstract: Dual-source trolleybuses alternate between overhead catenary supply and on-board battery operation, creating energy-use patterns driven by route attributes, high-frequency trajectories, and hourly weather.
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.
arXiv:2607. 20493v1 Announce Type: new Abstract: Deep learning has led to remarkable progress in artificial intelligence, particularly in robotics, imaging and sound processing.
The tutorial titled "Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers" explores how modern deep learning techniques—such as neural networks, transformers, large language models, and deep reinforcement learning—can be integrated with operations research and management science to address complex, uncertain, and dynamic decision problems. It argues that deep learning should complement, not replace, optimization, offering adaptability and scalable approximation while OR/MS provides rigorous constraint and uncertainty modeling. The tutorial organizes the field around predict‑then‑optimize, decision‑aware learning, constraint‑aware decision generation, and deep reinforcement learning, and highlights applications across supply chains, healthcare, energy, and autonomous systems.
arXiv:2605. 00015v2 Announce Type: replace-cross Abstract: Time Series Foundation Models (TSFMs) have demonstrated strong generalization capability and data efficiency in time series forecasting through large-scale pretraining.
Dual-source trolleybuses alternate between overhead catenary supply and on-board battery operation, creating energy-use patterns driven by route attributes, high-frequency trajectories, and hourly weather. Existing models struggle to represent these heterogeneous inputs and rarely explain the causal drivers of consumption.