A study on inventory control system for a supply chain using Markov decision processes

Abstract

This paper examines the application of Markov Decision Processes (MDPs) for controlling supply chain inventories. MDPs effectively simulate decision-making problems related to uncertainty, facilitating the determination of optimal inventory policies. The MDP framework addresses various inventory management challenges, including demand fluctuations, lead times, and holding costs. The study investigates the modeling of inventory management as a Markov decision process, detailing the states, actions, and transitions within the MDP model, along with their respective advantages and disadvantages. The research employs policy and value iteration techniques to evaluate and optimize inventory management policies. The paper assesses the proposed MDP-based inventory control system through simulations that utilize supply chain data, aiming to identify optimal policies using the MDP model. A comparative analysis of the MDP approach against conventional inventory management methods is conducted to demonstrate its efficacy in reducing costs and enhancing service levels. Additionally, the paper proposes the incorporation of multiple commodities, multi-echelon supply chains, and perishability considerations into the MDP model. The findings indicate that MDPs facilitate improved optimization of inventory policies, cost reductions, and enhanced customer service, thereby emphasizing the significance of computational complexity and the necessity for accurate data. This research provides a comprehensive investigation into the role of MDPs within inventory control systems, contributing valuable insights to the field of supply chain management. Ultimately, this study lays the groundwork for advancements in MDP-based inventory control methodologies.