arXiv:2607. 21354v1 Announce Type: new Abstract: For years, supply chain planning at e-commerce firms has operated as a collection of isolated projects.
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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.
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.
arXiv:2508. 02721v2 Announce Type: replace-cross Abstract: While powerful, the inherent non-determinism of large language model (LLM) agents limits their application in structured operational environments where procedural fidelity and predictable execution are strict requirements.
arXiv:2607. 28488v1 Announce Type: cross Abstract: Can supply-chain AI move beyond isolated decision modules toward unified operational planning?
The paper investigates whether large language models (LLMs) can design effective algorithms for well-specified operations research (OR) problems, focusing on inventory control, queueing network control, and assortment optimization. Two usage levels are examined: (1) the model receives a single problem instance and outputs a solution, and (2) the model receives only a problem class description and returns a general algorithm mapping instance parameters to solutions. Using a single untuned prompt and a Python sandbox, the strongest tested model, gpt-5.6-sol, matches or surpasses existing methods on nearly all evaluated instances, even when the algorithm is fixed before seeing evaluation cases, and performance improves markedly across models released within eight months.
The paper introduces OSCAR, an LLM‑based framework that translates business descriptions into accurate optimization models while verifying and improving them through a simulator, coder, and reviewer. OSCAR uses a cost‑ordered escalation strategy to select among LLMs of varying price and capability, achieving 95–100% accuracy on benchmark problems with local, open‑weight models. The framework also provides competitive guarantees and token‑cost advantages over existing LLMs like Codex and Claude Code.