Deep reinforcement learning in smart manufacturing: A review and prospects
To facilitate the personalized smart manufacturing paradigm with cognitive automation
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
A review on reinforcement learning algorithms and applications in supply chain management
Decision-making in supply chains is challenged by high complexity, a combination of
continuous and discrete processes, integrated and interdependent operations, dynamics …
continuous and discrete processes, integrated and interdependent operations, dynamics …
Applications of artificial intelligence in inventory management: A systematic review of the literature
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 …
must be able to effectively manage their supply chains to achieve high quality, increased …
[HTML][HTML] A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality
Y Tadayonrad, AB Ndiaye - Supply Chain Analytics, 2023 - Elsevier
Forecasting demand and determining safety stocks are key aspects of supply chain
planning. Demand forecasting involves predicting future demand for a product or service …
planning. Demand forecasting involves predicting future demand for a product or service …
Integrating machine learning, radio frequency identification, and consignment policy for reducing unreliability in smart supply chain management
Adopting smart technologies for supply chain management leads to higher profits. The
manufacturer and retailer are two supply chain players, where the retailer is unreliable and …
manufacturer and retailer are two supply chain players, where the retailer is unreliable and …
Adaptive supply chain: Demand–supply synchronization using deep reinforcement learning
Z Kegenbekov, I Jackson - Algorithms, 2021 - mdpi.com
Adaptive and highly synchronized supply chains can avoid a cascading rise-and-fall
inventory dynamic and mitigate ripple effects caused by operational failures. This paper …
inventory dynamic and mitigate ripple effects caused by operational failures. This paper …
[HTML][HTML] Big data driven order-up-to level model: Application of machine learning
JBB Clausen, H Li - Computers & Operations Research, 2022 - Elsevier
Data driven optimisation has become one of the research frontiers in operations
management and operations research. Likewise, the recent academic interest in big data …
management and operations research. Likewise, the recent academic interest in big data …
[HTML][HTML] Distributional reinforcement learning for inventory management in multi-echelon supply chains
G Wu, MÁ de Carvalho Servia, M Mowbray - Digital Chemical Engineering, 2023 - Elsevier
Reinforcement Learning (RL) is an effective method to solve stochastic sequential decision-
making problems. This is a problem description common to supply chain operations …
making problems. This is a problem description common to supply chain operations …
[HTML][HTML] An analysis of multi-agent reinforcement learning for decentralized inventory control systems
M Mousa, D van de Berg, N Kotecha… - Computers & Chemical …, 2024 - Elsevier
Most solutions to the inventory management problem assume a centralization of information
that is incompatible with organisational constraints in supply chain networks. The problem …
that is incompatible with organisational constraints in supply chain networks. The problem …
Feedback-based deterministic optimization is a robust approach for supply chain management under demand uncertainty
Optimization-based inventory and supply chain management (SCM) under uncertainty can
provide organizations a significant competitive advantage. Implementing optimization under …
provide organizations a significant competitive advantage. Implementing optimization under …