Certified Inventory Control of Critical Resources
This addresses inventory management for businesses needing reliable stock levels, but it appears incremental as it builds on existing online learning methods.
The paper tackles the problem of inventory control with service-level requirements by proposing a data-driven order policy that certifies any prescribed service level under minimal assumptions on unknown demand, using online learning and integral action, and demonstrates its properties with synthetic and real-world data.
Inventory control is subject to service-level requirements, in which sufficient stock levels must be maintained despite an unknown demand. We propose a data-driven order policy that certifies any prescribed service level under minimal assumptions on the unknown demand process. The policy achieves this using any online learning method along with integral action. We further propose an inference method that is valid in finite samples. The properties and theoretical guarantees of the method are illustrated using both synthetic and real-world data.