In the last 25 years, extensive research has been carried out addressing the flexible job
shop scheduling (JSS) problem. A variety of techniques ranging from exact methods to …

The flexible job shop scheduling problem (FJSP) is an NP-hard combinatorial optimization
problem, which has wide applications in the real world. The complexity and relevance of the …

This paper presents an end-to-end deep reinforcement framework to automatically learn a
policy for solving a flexible Job-shop scheduling problem (FJSP) using a graph neural …

As an important branch of production scheduling, flexible job-shop scheduling problem
(FJSP) is difficult to solve and is proven to be NP-hard. Many intelligent algorithms have …

Manufacturing enterprises nowadays face huge environmental challenges because of
energy consumption and associated environmental impacts. One of the effective strategies …

The key challenge of expensive optimization problems (EOP) is that evaluating the true
fitness value of the solution is computationally expensive. A common method to deal with …

Flexible job shop scheduling problem (FJSP) is an NP‐hard combinatorial optimisation
problem, which has significant applications in the real world. Due to its complexity and …

Flexible job-shop scheduling problem (FJSP) is very important in many research fields such
as production management and combinatorial optimization. The FJSP problems cover two …

There is currently no standard or widely accepted subset of features to effectively classify
different emotions based on electroencephalogram (EEG) signals. While combining all …

The previous studies on the flexible job shop scheduling problems (FJSP) with machine
flexibility and worker flexibility normally assume that each machine is operated by one …