The ability to understand and explain the outcomes of data analysis methods, with regard to
aiding decision-making, has become a critical requirement for many applications. For …

Today, companies that want to keep up with technological development and globalization
must be able to effectively manage their supply chains to achieve high quality, increased …

Industry 4.0 connotes a new industrial revolution centered around cyber-physical systems. It
posits that the real-time connection of physical and digital systems, along with new enabling …

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

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

Odimarha, Ayodeji, & Abaku, P. 725-740 Page 726 carbon emissions. By analyzing factors
such as traffic patterns, weather conditions, and road conditions, ML algorithms can …

This study addresses the optimal inventory management problem for new smartphone
products as an effective example of a supply chain with a short product life cycle. The …

Logistics creates tremendous economic value through supporting the trading of goods
between firms and customers, thereby improving the welfare of the society. In order to …

Problem definition: The beer game is widely used in supply chain management classes to
demonstrate the bullwhip effect and the importance of supply chain coordination. The game …

Deep reinforcement learning has been coined as a promising research avenue to solve
sequential decision-making problems, especially if few is known about the optimal policy …