An adaptive ensemble deep forest based dynamic scheduling strategy for low carbon flexible job shop under recessive disturbance

Abstract

Recessive disturbance can gradually lead to machine idling and production status deviation. Its instant influence on system performance is often insignificant. Still, it can be accumulated over time, consequently causing considerable unnecessary carbon emission and flexible system performance degradation, which brings many difficulties to production managers to make a timely and effective response. To cope with this problem, this paper proposes an adaptive hybrid dynamic scheduling strategy for low carbon flexible job shops, which helps production managers understand the production status of the flexible system and decide the optimal strategy to re-optimise the schedule. This strategy consists of two parts: decision feature and decision approach. For one, concerning performance, phase, and adaption capability (PPC), a decision feature is devised to quantify the dynamic production status. For the other, an ensemble deep forest-based dynamic scheduling decision approach is presented to adaptively select the optimal strategy from four typical dynamic scheduling strategies to accommodate schedules to recessive disturbances. The experiments are conducted to verify the effectiveness of the proposed strategy, and the results reveal the proposed strategy delivers excellent performances both in decision accuracy and schedule repairing.