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17Reinforcement Learning Approach in Supply Chain Management: A Review

Rajkanwar Singh¹, Pratik Mandal¹ and Sukanta Nayak^2*

¹School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India

²Department of Mathematics, School of Advanced Sciences, VIT-AP University, Amaravati, Andhra Pradesh, India

Abstract

Supply Chain Management (SCM) is one of the emerging areas that involve complex and challenging systems with multiple stakeholders and activities, ranging from demand forecasting to logistics management. The occurrence of complexity, demand volatility, cost-effectiveness, and information-sharing issues poses significant obstacles to efficient SCM functioning. In addition to these, the unprecedented global issues have further disrupted global supply chains. As such, there is an immediate need for an effective solution than ever before to handle these problems. In this context, Machine Learning (ML) techniques, such as Artificial Neural Networks (ANNs), Time-Series Analysis, Deep Learning, and Reinforcement Learning (RL), have been researched and used to tackle these challenges in SCM. Among these, RL has emerged as a promising technology to handle SCM activities such as inventory management, production scheduling, transportation and logistics optimization, demand forecasting, supplier selection and management, and risk management. RL is a type of machine learning in which an agent learns to interact with its environment by taking actions and ...