In order to respond to climate change, different governments set their own carbon neutrality
targets by considering their own realities. Under such a circumstance, manufacturing …

Against the realistic background of excess production capacity, product structure imbalance,
and high material and energy consumption in steel enterprises, the implementation of …

A reinforcement learning-driven brain storm optimisation idea (RLBSO) is proposed in this
paper to solve multi-objective energy-efficient distributed assembly no-wait flow shop …

The distributed flow shop scheduling problem has become one of the key problems related
to the high efficiency impacted factor in the manufacturing industry due to its typical …

The integration of reinforcement learning technology into meta-heuristic algorithms to
address complex combinatorial optimization problems has attracted much attention in recent …

As the performance of devices that conduct large-scale computations has been rapidly
improved, various deep learning models have been successfully utilized in various …

The development of technology accelerates the upgrade of products, which results in a
significant number of obsolete products. This research aims to solve the Multi-robotic Multi …

As a novel neural network-based learning framework, a broad learning system (BLS) has
attracted much attention due to its excellent performance on regression and balanced …

Recent years have witnessed the use of metaheuristic algorithms to solve the optimization
problems that usually require extensive computations and time. Among others, scatter …

With the development of the economy and technology, distributed manufacturing and green
manufacturing are becoming more and more important parts of intelligent manufacturing. In …