Job shop scheduling problems (JSSPs) represent a critical and challenging class of
combinatorial optimization problems. Recent years have witnessed a rapid increase in the …

In recent years, in the field of Multi-Agent Systems (MAS), significant progress has been
made in the research of algorithms that combine Reinforcement Learning (RL) with Attention …

The green flexible job shop has received increasing attention due to the development of
modern industry and the improvement of environmental protection awareness. Meanwhile …

Abstract The Flexible Job Shop Scheduling Problem (FJSP), a classic NP-hard optimization
challenge, has a direct impact on manufacturing system efficiency. Considering that the …

The Distributed Job-shop Scheduling Problem (DJSP) is a significant issue in both
academic and industrial fields. In real-world production, uncertain disturbances such as job …

Recent advancements in the flexible job-shop scheduling problem (FJSSP) are primarily
based on deep reinforcement learning (DRL) due to its ability to generate high-quality, real …

Abstract The Multi-Objective Flexible Job Shop Scheduling Problem (MOFJSP) is a complex
challenge in manufacturing, requiring balancing multiple, often conflicting objectives …

Abstract Machine scheduling aims to optimally assign jobs to a single or a group of
machines while meeting manufacturing rules as well as job specifications. Optimizing the …

Graph neural networks (GNNs) have emerged as a powerful tool for solving combinatorial
optimization problems (COPs), exhibiting state-of-the-art performance in both graph …

The flexible job shop scheduling problem (FJSSP), which can significantly enhance
production efficiency, is a mathematical optimization problem widely applied in modern …