Abstract Machine Learning (ML) is a branch of artificial intelligence that studies algorithms
able to learn autonomously, directly from the input data. Over the last decade, ML …

Reinforcement learning (RL) is one of the most remarkable branches of machine learning
and attracts the attention of researchers from numerous fields. Especially in recent years, the …

We propose a framework to learn to schedule a job-shop problem (JSSP) using a graph
neural network (GNN) and reinforcement learning (RL). We formulate the scheduling …

In modern manufacturing industry, dynamic scheduling methods are urgently needed with
the sharp increase of uncertainty and complexity in production process. To this end, this …

This paper provides a novel intelligent scheduling strategy for a real-world transportation
dynamic scheduling case from an engine workshop of general motor company (GMEW) …

Traditional approaches for job shop scheduling problems are ill-suited to deal with complex
and changeable production environments due to their limited real-time responsiveness …

Flexible job shop scheduling problem (FJSP) has attracted research interests as it can
significantly improve the energy, cost, and time efficiency of production. As one type of …

In modern volatile and complex manufacturing environment, dynamic events such as new
job insertions and machine breakdowns may randomly occur at any time and different …

Manufacturing is involved with complex job shop scheduling problems (JSP). In smart
factories, edge computing supports computing resources at the edge of production in a …

Driven by the recent advances in industry 4.0 and industrial artificial intelligence, Automated
Guided Vehicles (AGVs) has been widely used in flexible shop floor for material handling …