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 …

Recently, deep reinforcement learning (DRL) has been applied to learn priority dispatching
rules (PDRs) for solving complex scheduling problems. However, the existing works face …

Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling
problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad …

In the past decades, many optimization methods have been devised and applied to job shop
scheduling problem (JSSP) to find the optimal solution. Many methods assumed that the …

In recent years, the uncertainties and complexity in the production process, due to the
boosted customized requirements, has dramatically increased the difficulties of Dynamic …

The flexible job shop scheduling (FJSS) is important in real-world factories due to the wide
applicability. FJSS schedules the operations of jobs to be executed by specific machines at …

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 …

Deep reinforcement learning (DRL), as a promising technique, is a new approach to solve
the job shop scheduling problem (JSSP). Although DRL method is effective for solving …

The job-shop scheduling problem (JSSP) is a classical NP-hard combinatorial optimization
problem, and the operating efficiency of manufacturing system is affected directly by the …

Due to the influence of dynamic changes in the manufacturing environment, a single
dispatching rule (SDR) cannot consistently attain better results than other rules for dynamic …