Certified Inventory Control of Critical Resources
Ludvig Hult, Dave Zachariah, Petre Stoica

TL;DR
This paper introduces a data-driven inventory control policy that guarantees service levels with minimal assumptions, using online learning and finite-sample inference, validated on synthetic and real data.
Contribution
It presents a novel certified inventory control policy that ensures service levels under minimal demand assumptions, combining online learning with finite-sample inference.
Findings
Guarantees service levels with minimal demand assumptions
Validates approach with synthetic and real-world data
Provides finite-sample inference method
Abstract
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.
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Taxonomy
TopicsFlexible and Reconfigurable Manufacturing Systems
Methodstravel james
