A job shop scheduling system based on Parallel Control Mechanism

Job shop scheduling has been an active area of research for several decades. However, there seems to have been a significant gap between the theoretical research in academia and practical application in industry. The scheduling system, which are developed aiming at the vast library of benchmark problems, usually faces some challenges when applying in the real manufacturing environment to support the day-to-day running of a factory. This paper will present a job shop scheduling system, which is inspired by the Parallel Control Mechanism. The system consists of two interacting subsystems, the artificial sub-system, which could provide optimal scheduling scheme, and the actual one, which would re-generate the schedule according to the real would situation. A hybrid scheduling strategy is proposed, which supports the system to make scheduling decision in job shop that operates in a dynamic environment. The strategy harmonize the different approaches, ant colony optimization algorithm, rule-based scheduling, or real-time simulation, into a unified framework, and therefore can be used as an online scheduler. The presented system and strategy offers better performance than conventional single-optimal-algorithm-based scheduling system.