Decision-making in supply chains is challenged by high complexity, a combination of
continuous and discrete processes, integrated and interdependent operations, dynamics …

With advances in technologies, data science techniques, and computing equipment, there
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …

Modern production systems face enormous challenges due to rising customer requirements
resulting in complex production systems. The operational efficiency in the competitive …

The objective of this paper is to examine the use and applications of reinforcement learning
(RL) techniques in the production planning and control (PPC) field addressing the following …

Reinforcement learning (RL) appeals to many researchers in recent years because of its
generality. It is an approach to machine intelligence that learns to achieve the given goal by …

Deep reinforcement learning (DRL) has shown great potential for sequential decision-
making, including early developments in inventory control. Yet, the abundance of choices …

A major issue in supply chain inventory management is the coordination of inventory
policies adopted by different supply chain actors, such as suppliers, manufacturers …

One of the challenging issues in supply chain management is the coordination of ordering
processes, especially in dynamic situations. In recent years, reinforcement learning …

Reinforcement learning (RL) has been widely applied to game-playing and surpassed the
best human-level performance in many domains, yet there are few use-cases in industrial or …

In recent years, reinforcement learning (RL) has attracted significant attention from both
industry and academia due to its success in solving some complex problems. This paper …