arXiv:2606. 25362v1 Announce Type: cross Abstract: Sequential contextual stochastic programs model real-time decision systems in which each time epoch commits to an action under uncertainty whose consequences propagate into future decisions.
arXiv:2307. 05213v3 Announce Type: replace-cross Abstract: Many real-world optimization problems contain parameters that are unknown before deployment time, either due to stochasticity or to lack of information (e.
arXiv:2607. 04056v1 Announce Type: cross Abstract: Modern supply chains span diverse operational environments, ranging from e-commerce distribution networks to customized production-to-order manufacturing lines.
arXiv:2608. 02343v1 Announce Type: cross Abstract: Many operational problems are constrained sequential decision processes with large, combinatorial action spaces and interdependent feasibility constraints.
arXiv:2607. 16875v1 Announce Type: cross Abstract: We introduce the vehicle routing problem with stochastic demands and outsourcing options (VRP-SDO), in which a logistics service provider partitions customer requests into customers outsourced to a common carrier and customers committed to its fixed fleet.
arXiv:2607. 10694v1 Announce Type: cross Abstract: We study the problem of optimal continual fine-tuning for a pre-trained Foundation Model deployed at a resource-limited device.
arXiv:2609. 28263v1 Announce Type: new Abstract: The growth of large language model (LLM) inference and search services increases the scale of online linear programming problems, motivating computationally efficient algorithms.
The paper introduces PORL, a hybrid method that first trains a general scheduling policy through online reinforcement learning in simulation, then fine‑tunes it offline on production data using a KL‑divergence constraint to limit policy drift. PORL is evaluated on Job Shop Scheduling Problem instances with distribution shifts and various data sources, consistently outperforming standalone offline RL and other baselines, especially when offline data quality is low. The results suggest that offline adaptation of pretrained policies can improve industrial scheduling when direct online exploration is impractical.
arXiv:2607. 11725v1 Announce Type: cross Abstract: Prefabricated prefinished volumetric construction moves most building work into module factories, whose production floor operates as a flexible job shop.
The paper introduces a reinforcement learning framework that selects among a portfolio of gradient‑based and derivative‑free optimizers during a run. At each decision point a recurrent policy reads the current run state and chooses both the next optimizer and its usage duration, passing the best solution and step size forward. The method is trained with a decoupled actor‑critic using the same runtime distribution metric as evaluation, and on unseen problems it outperforms all individual portfolio optimizers except at the smallest budgets, remaining robust to distribution shift.
arXiv:2610.01269v1 Announce Type: cross Abstract: Bayesian Optimisation (BO) is a powerful framework for the optimisation of expensive black-box functions, but typically requires refitting a surrogat...
arXiv:2510. 17709v2 Announce Type: replace-cross Abstract: Training Reinforcement Learning (RL) policies using simulation models before deployment in real-world environments is a common strategy when real-world interaction is expensive.
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.