Hybrid flow shop dynamic scheduling method based on improved random forest and reinforcement learning

doi:10.16731/j.cnki.1671-3133.2024.11.004

Abstract

Abstract: In order to adapt the production demands of hybrid flow shop, a two-stage dynamic scheduling method based on machine learning is proposed.In the offline mining phase, based on historical data, the improved random forest algorithm is used to establish a knowledge mapping network from the production state of the manufacturing system to the optimal scheduling rules, which can be mined for online decision-making, thus skipping the warm-up phase to improve the scheduling efficiency and optimize the scheduling scheme. In the online scheduling phase, the reinforcement learning algorithm is used to analyze and train the real-time data of the flow shop state. And it optimizes the strategy selection according to the system state′s dynamic changes of the system state to achieve the adaptive and fast response capability to the perturbation events. The simulation experiment verified that the two-phase dynamic scheduling approach combining data mining and reinforcement learning is more feasible and effective for fully utilizing manufacturing data and scheduling the manufacturing execution process online.

Key words: hybrid flow shop, dynamic scheduling, reinforcement learning, improved random forest, data-driven

CLC Number:

TP18

ZHANG Mengjie, YANG Xiaoying, LI Bo. Hybrid flow shop dynamic scheduling method based on improved random forest and reinforcement learning[J]. Modern Manufacturing Engineering, 2024, 530(11): 26-36.

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