Deep Reinforcement Learning for Inventory Optimization with Non-Stationary Uncertain Demand

Henri Dehaybe (), Daniele Catanzaro () and Philippe Chevalier
Additional contact information
Henri Dehaybe: Université catholique de Louvain, LIDAM/CORE, Belgium
Daniele Catanzaro: Université catholique de Louvain, LIDAM/CORE, Belgium

No 3270, LIDAM Reprints CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: We consider here a single-item lot sizing problem with fixed costs, lead time, and both backorders and lost sales, and we show that, after an appropriate training in randomly generated environments, Deep Reinforcement Learning (DRL) agents can interpolate in real-time near-optimal dynamic policies on instances with a rolling-horizon, provided a previously unseen demand forecast and without the need to periodically resolve the problem. Extensive computational experiments show that the policies provided by these agents compete, and in some circumstances even outperform by several percentage points of gap, those provided by heuristics based on dynamic programming. These results confirm the importance of DRL in the context of inventory control problems and support its use in solving practical instances featuring realistic assumptions.

Keywords: Inventory; Lot Sizing; Forecast Evolution; Deep Reinforcement Learning; Non-Stationary Demand (search for similar items in EconPapers)
Pages: 16
Date: 2023-10-01
Note: In: European Journal of Operational Research, 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: Deep Reinforcement Learning for inventory optimization with non-stationary uncertain demand (2024)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cor:louvrp:3270

DOI: 10.1016/j.ejor.2023.10.007

Access Statistics for this paper

More papers in LIDAM Reprints CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) Voie du Roman Pays 34, 1348 Louvain-la-Neuve (Belgium). Contact information at EDIRC.
Bibliographic data for series maintained by Alain GILLIS ().