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: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:2607. 02941v1 Announce Type: new Abstract: Multi-product kitting delivery imposes significant challenges for real-time scheduling in hybrid manufacturing systems that integrate processing and assembly, as dynamic order arrivals simultaneously alter supply dependencies and the set of feasible job-machine assignments.
arXiv:2607. 28488v1 Announce Type: cross Abstract: Can supply-chain AI move beyond isolated decision modules toward unified operational planning?
arXiv:2606. 18820v1 Announce Type: cross Abstract: Sequential decision problems often exhibit an asymmetric evolution of information and decision flexibility: as a decision cycle unfolds, the agent receives richer information while feasible actions expire due to operational cutoffs, commitments, or resource constraints.
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: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:2607. 21354v1 Announce Type: new Abstract: For years, supply chain planning at e-commerce firms has operated as a collection of isolated projects.
arXiv:2606. 26852v1 Announce Type: new Abstract: Order fulfillment in manual picker-to-goods warehouses involves interconnected decisions such as item assignment, order batching, and picker routing.
For years, supply chain planning at e-commerce firms has operated as a collection of isolated projects. Each planning task from static network planning to dynamic warehouse assortment planning requires analysts to spend weeks building models from scratch, calibrating and persuading executives to act on outputs they cannot verify.
The paper introduces a constraint‑aware conditional generative framework for creating synthetic origin‑destination demand data in hierarchical logistics networks. By modeling demand as a conditional distribution over destinations given each origin, the method incorporates differentiable operational constraints directly into the generative objective, allowing topology‑aware synthesis that remains operationally feasible. Experiments on industrial fulfillment and transportation networks show a 16% performance gain over graph neural network baselines, 87% operational compliance, and efficient cold‑start adaptation, supporting capacity planning, network design evaluation, and routing optimization.
arXiv:2607. 22788v1 Announce Type: cross Abstract: AC optimal power flow determines the minimum-cost generation dispatch under nonlinear power balance constraints and is solved thousands of times daily in electricity market operations.
The paper addresses the challenge of scheduling business process activities when the exact sequence of required tasks is uncertain due to data-driven decisions made during execution. It proposes framing the problem as a chance-constrained optimization and introduces two formulations: a decomposed two-stage approach that first minimizes expected superfluous activities under a feasibility constraint and then schedules to minimize makespan, and an integrated approach that combines planning and scheduling into a single model. Experiments on two real-world and one synthetic dataset show that the integrated approach achieves better makespans but struggles with scalability, whereas the decomposed approach scales to larger settings.