No abstract

Real-time scheduling methods are essential and critical to complex product flexible shop-floor due to the dynamic events in the production process, such as new job insertions, machine breakdowns and frequent rework. Recently, digital twin (DT) technology can help identify disturbances by continuously comparing physical space with virtual space, which enables real-time scheduling and greatly reduces the deviation between pre-schedule and actual schedule. However, the conventional scheduling models and algorithms cannot satisfy the adaptiveness and timeliness requirements of optimization in DT enabled shop-floor (DTS). To address above challenges, an overall framework of DT enabled real-time scheduling (DTE-RS) for complex product shop-floor is proposed to effectively reduce adverse impacts of the dynamic disturbances and minimize the makespan. Firstly, complex product flexible job shop scheduling problem (CPFJSP) is formulated as Markov Decision Process (MDP), taking into account machine breakdown and new job insertions. Then, deep Q-network (DQN) based solution is developed to achieve optimal task dispatching according to real-time production state. Finally, the case study for aircraft overhaul shop-floor is conducted to demonstrate effectiveness and feasibility of the proposed real-time scheduling method. Through experimental comparison, it is indicated that the proposed method could effectively respond to dynamic disturbances and outperform the dynamic scheduling method in terms of makespan.

The implementation strategy for meticulous production management of aircraft overhaul is facing barriers, such as the lack of an effective method that can achieve accurate prediction of makespan in the whole aircraft overhaul process. In this paper, a novel prediction method for makespan of aircraft overhaul by extending feature selection principal component analysis (FSPCA) and backpropagation (BP) neural network (FSPCA-BP) is proposed. Firstly, the FSPCA algorithm is developed to achieve feature selection and dimension reduction simultaneously. Then, the principal components factors obtained from the FSPCA algorithm are used as the input vectors for the BP neural network to predict the makespan of aircraft overhaul. Finally, the proposed method is demonstrated and tested in an application scenario of a partner company and is further compared with PCA-BP and BP approaches, respectively. The result shows that the FSPCA-BP approach has higher accuracy than PCA-BP and BP approaches in aircraft overhaul makespan prediction. Finally, the managerial implications of the proposed method for shop-floor departments are discussed in detail.

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.