LAPSE:2022.0009
Published Article
LAPSE:2022.0009
Algorithmic Approaches to Inventory Management Optimization
January 24, 2022
An inventory management problem is addressed for a make-to-order supply chain that has inventory holding and/or manufacturing locations at each node. The lead times between nodes and production capacity limits are heterogeneous across the network. This study focuses on a single product, a multi-period centralized system in which a retailer is subject to an uncertain stationary consumer demand at each time period. Two sales scenarios are considered for any unfulfilled demand: backlogging or lost sales. The daily inventory replenishment requests from immediate suppliers throughout the network are modeled and optimized using three different approaches: (1) deterministic linear programming, (2) multi-stage stochastic linear programming, and (3) reinforcement learning. The performance of the three methods is compared and contrasted in terms of profit (reward), service level, and inventory profiles throughout the supply chain. The proposed optimization strategies are tested in a stochastic simulation environment that was built upon the open-source OR-Gym Python package. The results indicate that, of the three approaches, stochastic modeling yields the largest increase in profit, whereas reinforcement learning creates more balanced inventory policies that would potentially respond well to network disruptions. Furthermore, deterministic models perform well in determining dynamic reorder policies that are comparable to reinforcement learning in terms of their profitability.
Keywords
inventory management, multi-echelon, reinforcement learning, stochastic programming, Supply Chain
Suggested Citation
Perez HD, Hubbs CD, Li C, Grossmann IE. Algorithmic Approaches to Inventory Management Optimization. (2022). LAPSE:2022.0009
Author Affiliations
Perez HD: Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15123, USA [ORCID]
Hubbs CD: Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15123, USA [ORCID]
Li C: Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15123, USA [ORCID]
Grossmann IE: Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15123, USA [ORCID]
Journal Name
Processes
Volume
9
Issue
1
First Page
pr9010102
Year
2021
Publication Date
2021-01-06
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr9010102, Publication Type: Journal Article
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LAPSE:2022.0009
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doi:10.3390/pr9010102
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Jan 24, 2022
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CC BY 4.0
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[v1] (Original Submission)
Jan 24, 2022
 
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Jan 24, 2022
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http://psecommunity.org/LAPSE:2022.0009
 
Original Submitter
Calvin Tsay
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