Journal
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume 127, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2023.107403
Keywords
Deep reinforcement learning; Perishable inventory; Inventory allocation; Vendor managed inventory; Policy gradient; Advantage actor-critic
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This article explores the complex issue of perishable inventory allocation within a two-echelon supply chain and proposes a deep reinforcement learning approach to tackle the challenges posed by uncertain demands and variable supply conditions. Empirical experiments using real-world data from a blood supply chain affirm the effectiveness of the proposed algorithm in addressing the inventory allocation problem, highlighting its significance in enhancing efficiency and operational excellence in perishable supply chains.
This article delves into the challenging supply chain management domain, explicitly addressing the intricate issue of perishable inventory allocation within a two-echelon supply chain. The approach outlined here leverages deep reinforcement learning with a keen understanding of the inherent stochasticity arising from uncertain demands and variable supply conditions. The examined supply chain encompasses two retailers and a central distribution center operating under a vendor-managed inventory system. The primary goal of this research is to combat the prevalent problems of wastage and shortages frequently encountered at the retail level in such supply chains. The study employs the Advantage Actor-Critic (A2C) algorithm, tailored to handle the continuous action space inherent in inventory allocation. To rigorously evaluate this approach, empirical data from a real-world blood supply chain in Tabriz is used for numerical experiments. This practical case study involves a single distribution center and two hospitals. The outcomes of these experiments affirm the effectiveness of the A2C algorithm, showcasing its ability to address the complex inventory allocation problem successfully. Furthermore, the research highlights that the algorithm outperforms existing supply chain policies, underscoring the pivotal role of optimal allocation in enhancing efficiency and operational excellence in perishable supply chains.
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