Energy-aware dynamic rescheduling of flexible manufacturing system using edge-cloud collaborative decision-making method

ABSTRACT

With the development of cloud computing technology, energy-aware scheduling based on cloud computing is an essential means to achieve energy saving and carbon reduction in manufacturing systems. Due to various production disturbances in flexible production, the scheduling based on cloud computing has data security problems and poor real-time performance. How to use real-time information to improve the security and responsiveness of cloud scheduling is a research gap. This paper establishes a cloud-edge collaborative dynamic flexible job-shop energy-aware rescheduling decision-making model. According to the characteristics of dynamic production in a flexible manufacturing system, create a learning model for cloud scheduling with Deep Q Network (DQN) and send the training model to the edge for scheduling decisions. Based on real-time production interference data, edge scheduling model real-time update scheduling scheme. To improve the robustness of the cloud scheduling model based on DQN, Dynamic scheduling data at the edge will be uploaded to update the cloud model. In addition, the effectiveness and practicability of the model are verified in an extrusion workshop. The experimental results show that this method can improve the comprehensive objective evaluation of minimum energy consumption and completion time by 3.6% −49.3% compared with the traditional scheduling rules.

KEYWORDS:

• Energy-aware dynamic rescheduling
• deep Q network
• edge-cloud collaboration
• flexible manufacturing system
• reinforcement learning

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author contributions

All authors contributed to the study conception and design. Material preparation and literature collection were performed by Jian Peng. Data collection were performed by Yubo Lei. The thesis topic and research framework were proposed by Huajun Cao and Yachao Jia. Yunpeng Cao designed the experiment and wrote the first draft of the manuscript. The revision of the manuscript was performed by Hongcheng Li.

Additional information

Funding

This work was supported by the National Key Research and Development Program of China [2022YFB3304500].