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. 05830v1 Announce Type: cross Abstract: The increasing uncertainty from flexible demand and renewable generation has made distributionally robust optimization (DRO) an important tool for robust power system dispatch.
arXiv:2607. 16769v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a powerful, differentiable class of learning models for graph-structured systems.
arXiv:2405. 01906v3 Announce Type: replace Abstract: In modern intelligent transportation systems (ITS), particularly in freight transportation and logistics, real-time route planning is crucial.
The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning. While Graph Neural Networks have emerged as fast, data-driven surrogates, their practical deployment is severely constrained by a spatial generalization gap.
The paper introduces a fully differentiable message‑passing neural network (MPNN) designed to approximate the Uniform Facility Location (UniFL) problem. Unlike many learning‑based approaches that require supervision or reinforcement learning, this model incorporates principles from classical approximation algorithms, providing provable approximation guarantees. Empirical results show that it outperforms standard approximation algorithms and reduces the performance gap to integer linear programming solutions.
arXiv:2607. 23467v1 Announce Type: new Abstract: We study an integrated pickup-and-delivery problem on sparse, non-Euclidean networks that jointly optimizes cyclic routing, cargo flow allocation, and cross-cycle service.
arXiv:2607. 19270v1 Announce Type: cross Abstract: The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning.
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: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.
The article "Supply Chain Analytics: A Data-Driven Approach" presents a mathematically rigorous framework that integrates statistical learning with robust decision-making for logistics and operations management. It covers topics from empirical demand forecasting to optimal inventory and network control under uncertainty, including sample minimization, dynamic programming for inventory replenishment, network fulfillment, and distributionally robust optimization using transport theory. The work also links predictive models with prescriptive algorithms such as column generation for vehicle routing and non‑homogeneous queueing regimes, offering both theoretical foundations and algorithmic guidance for building resilient, automated supply chain systems.
arXiv:2510.09373v2 Announce Type: replace Abstract: Constraint Programming (CP) offers an intuitive, declarative framework for modeling Vehicle Routing Problems (VRP). While classical successor-based...
arXiv:2608. 10245v1 Announce Type: cross Abstract: Inventory and distribution planning in Physical Internet networks requires coordinating factory-hub assignments, factory supply, lateral transshipment among collaborative hubs, retailer deliveries, and shortages.