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Article

Algorithmic Approaches to Inventory Management Optimization

Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15123, USA
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Author to whom correspondence should be addressed.
Processes 2021, 9(1), 102; https://doi.org/10.3390/pr9010102
Received: 1 December 2020 / Revised: 29 December 2020 / Accepted: 30 December 2020 / Published: 6 January 2021
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. View Full-Text
Keywords: inventory management; supply chain; multi-echelon; stochastic programming; reinforcement learning inventory management; supply chain; multi-echelon; stochastic programming; reinforcement learning
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MDPI and ACS Style

Perez, H.D.; Hubbs, C.D.; Li, C.; Grossmann, I.E. Algorithmic Approaches to Inventory Management Optimization. Processes 2021, 9, 102. https://doi.org/10.3390/pr9010102

AMA Style

Perez HD, Hubbs CD, Li C, Grossmann IE. Algorithmic Approaches to Inventory Management Optimization. Processes. 2021; 9(1):102. https://doi.org/10.3390/pr9010102

Chicago/Turabian Style

Perez, Hector D., Christian D. Hubbs, Can Li, and Ignacio E. Grossmann 2021. "Algorithmic Approaches to Inventory Management Optimization" Processes 9, no. 1: 102. https://doi.org/10.3390/pr9010102

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