Reinforcement learning applied to production planning and control
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 …
(RL) techniques in the production planning and control (PPC) field addressing the following …
Combinatorial optimization and reasoning with graph neural networks
Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …
science. Until recently, its methods have focused on solving problem instances in isolation …
A survey for solving mixed integer programming via machine learning
Abstract Machine learning (ML) has been recently introduced to solving optimization
problems, especially for combinatorial optimization (CO) tasks. In this paper, we survey the …
problems, especially for combinatorial optimization (CO) tasks. In this paper, we survey the …
[HTML][HTML] A deep reinforcement learning hyper-heuristic with feature fusion for online packing problems
In recent years, deep reinforcement learning has shown great potential in solving computer
games with sequential decision-making scenarios. Hyper-heuristic is a generic search …
games with sequential decision-making scenarios. Hyper-heuristic is a generic search …
[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 organizational constraints in supply chain networks. The problem …
that is incompatible with organizational constraints in supply chain networks. The problem …
Algorithmic approaches to inventory management optimization
An inventory management problem is addressed for a make-to-order supply chain that has
inventory holding and/or manufacturing locations at each node. The lead times between …
inventory holding and/or manufacturing locations at each node. The lead times between …
Rl4co: an extensive reinforcement learning for combinatorial optimization benchmark
We introduce RL4CO, an extensive reinforcement learning (RL) for combinatorial
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …
Deep reinforcement learning assisted genetic programming ensemble hyper-heuristics for dynamic scheduling of container port trucks
Efficient truck dispatching is crucial for optimizing container terminal operations within
dynamic and complex scenarios. Despite good progress being made recently with more …
dynamic and complex scenarios. Despite good progress being made recently with more …
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] Combining deep reinforcement learning and multi-stage stochastic programming to address the supply chain inventory management problem
We introduce a novel heuristic designed to address the supply chain inventory management
problem in the context of a two-echelon divergent supply chain. The proposed heuristic …
problem in the context of a two-echelon divergent supply chain. The proposed heuristic …