An Application of Deep Reinforcement Learning in Novel Supply Chain Management Approaches for Inventory Control and Management of Perishable Supply Chain Network

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Mohammadi, Navid | 2021

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 54322 (01)
University: Sharif University of Technology
Department: Industrial Engineering
Advisor(s): Akhavan Niaki, Taghi
Abstract:
This study proposes a deep reinforcement learning approach to solve a perishable inventory allocation problem in a two-echelon supply chain. The inventory allocation problem is studied considering the stochastic nature of demand and supply. The examined supply chain includes two retailers and one distribution center (DC) under a vendor-managed inventory (VMI) system. This research aims to minimize the wastages and shortages occurring at the retailer's sites in the examined supply chain. With regard to continuous action space in the considered inventory allocation problem, the Advantage Actor-Critic algorithm is implemented to solve the problem. Numerical experiments are implemented on actual data gathered from a blood supply chain located in Tabriz with one distribution center and two hospitals to test the performance of the proposed approach. The results demonstrate that the Advantage Actor-Critic algorithm successfully solves the considered inventory allocation problem and converges to a near-optimal solution
Keywords:
Deep Reinforcement Learning ; Perishable Inventory ; Vendor Managed Inventory (VMI) ; Supply Chain ; Inventory Management ; Stock Management by Seller ; Perishable Supply Chain ; Inventory Allocation ; Blood Supply Chain

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