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

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 …

Abstract Machine Learning (ML) is revolutionizing supply chain and logistics optimization in
the oil and gas sector. This comprehensive analysis explores how ML algorithms are …

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 …

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 …

This work provides a Deep Reinforcement Learning approach to solving a periodic review
inventory control system with stochastic vendor lead times, lost sales, correlated demand …

Abstract The One-Warehouse Multi-Retailer (OWMR) system is the prototypical distribution
and inventory system. Many OWMR variants exist, eg demand in excess of supply may be …

Reference prices, developed by consumers who frequently buy their desired products or
services and form psychological price expectations as a benchmark, have significant …

More brick-and-mortar retailers open an online channel to increase sales. Often, they use
the store to fulfil online orders and to receive returned products. The uncertain product …