Solver-Verified Formulation Generation and Selection for Multi-Warehouse Inventory Allocation Using Large Language Models
arXiv:2606.29366v1 Announce Type: cross Abstract: Balance-oriented multi-warehouse inventory allocation is a recurring decision problem in large-scale e-commerce supply chains, in which a fixed replenishment quantity is distributed across warehouses to balance post-allocation inventory coverage while accounting for demand forecasts and heterogeneous allocation constraints. In practice, allocation requirements are often scenario-dependent and expressed in semi-structured or natural-language form rather than as ready-to-solve operations research (OR) formulations. We propose an OR-guided Large L
The increasing sophistication of Large Language Models (LLMs) combined with the growing complexity of global supply chains makes this a timely development for optimizing logistics.
This development indicates a powerful application of AI to solve complex operational research problems, potentially leading to significant efficiencies and competitive advantages in supply chain management.
Traditional manual or expert-driven formulation of operations research problems for inventory allocation can now be automated and verified by AI, accelerating deployment and adaptability.
- · E-commerce companies (large scale)
- · Logistics and supply chain software providers
- · AI/ML developers
- · Operations research practitioners
- · Companies slow to adopt AI in supply chain
- · Traditional OR consulting firms (without AI integration)
Increased efficiency and reduced costs in multi-warehouse inventory allocation for large e-commerce operations.
Broader adoption of AI-driven optimization across various complex business processes, extending beyond supply chain.
Potential for an AI 'operating system' that dynamically reformulates and solves high-level business objectives across an entire enterprise.
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Read at arXiv cs.AI