An adaptive scheduling approach with agent and Q-learning for aircraft overhaul shop-floor

Shifeng Fu; Xiaoliang Jia; Hao Hu; Xiao Chang

doi:10.1117/12.2667657

28 March 2023 An adaptive scheduling approach with agent and Q-learning for aircraft overhaul shop-floor

Shifeng Fu, Xiaoliang Jia, Hao Hu, Xiao Chang

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 1256634 (2023) https://doi.org/10.1117/12.2667657
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

Abstract

Typical challenges that aircraft overhaul enterprises faced are the lack of timely, accurate scheduling of operational and inter-operational tasks in shop-floor level during the whole overhaul process, resulting in long overhaul cycle, rising cost, and labor. Therefore, it is extremely important to establish rapid-response troubleshooting methods considering the complexity of overhaul process uncertainty and disturbances to improve shop-floor performance. To address these challenges, a novel agent-based Aircraft Overhaul Adaptive Scheduling Framework (AOASF) is proposed to realize the rapid assignment of aircraft overhaul tasks. Within this framework, a scheduling method using Q-learning is designed, which aims to rapid-responding the disturbances in time and achieve adaptive scheduling. A simulation system is implemented to verify the effectiveness and superiority of the AOASF and the scheduling method. The simulation results show that the method is superior to conventional scheduling methods in terms of make span and Percent Tardy (PT), especially when tasks arrive frequently.

Citation Download Citation

Shifeng Fu, Xiaoliang Jia, Hao Hu, and Xiao Chang "An adaptive scheduling approach with agent and Q-learning for aircraft overhaul shop-floor", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 1256634 (28 March 2023); https://doi.org/10.1117/12.2667657

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