Efficient truck dispatching is crucial for optimizing container terminal operations within
dynamic and complex scenarios. Despite good progress being made recently with more …

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 …

Most solutions to the inventory management problem assume a centralization of information
that is incompatible with organisational constraints in supply chain networks. The problem …

This study conducts a comprehensive analysis of deep reinforcement learning (DRL)
algorithms applied to supply chain inventory management (SCIM), which consists of a …

The dramatic growth of global e-commerce has led to a surge in demand for efficient and
cost-effective order fulfillment which can increase customers' service levels and sellers' …

In this paper, we address the difficulty of solving large-scale multi-dimensional knapsack
instances (MKP), presenting a novel deep reinforcement learning (DRL) framework. In this …

Reinforcement learning (RL) is a promising solution for difficult decision-making problems,
such as inventory management in chemical supply chains. However, enabling RL to …

Efficient generalisation in supply chain inventory management is challenging due to a
potential mismatch between the model optimised and objective reality. It is hard to know how …

Reinforcement learning (RL) algorithms have proven to be useful tools for combinatorial
optimisation. However, they are still underutilised in facility layout problems (FLPs). At the …

Spanning tree problems with specialized constraints can be difficult to solve in real-world
scenarios, often requiring intricate algorithmic design and exponential time. Recently, there …