The production process of a smart factory is complex and dynamic. As the core of manufacturing management, the research into the flexible job shop scheduling problem (FJSP) focuses on optimizing scheduling decisions in real time, according to the changes in the production environment. In this paper, deep reinforcement learning (DRL) is proposed to solve the dynamic FJSP (DFJSP) with random job arrival, with the goal of minimizing penalties for earliness and tardiness. A double deep Q-networks (DDQN) architecture is proposed and state features, actions and rewards are designed. A soft ε-greedy behavior policy is designed according to the scale of the problem. The experimental results show that the proposed DRL is better than other reinforcement learning (RL) algorithms, heuristics and metaheuristics in terms of solution quality and generalization. In addition, the soft ε-greedy strategy reasonably balances exploration and exploitation, thereby improving the learning efficiency of the scheduling agent. The DRL method is adaptive to the dynamic changes of the production environment in a flexible job shop, which contributes to the establishment of a flexible scheduling system with self-learning, real-time optimization and intelligent decision-making.
In various circumstances, such as human-robot interactions and industrial processes, planning at trajectory level is very useful to produce better movement. In this paper we present a near time optimal approach to plan a trajectory joining via points in real time for robot manipulators. To limit the speed variation, the path is smoothed around via-points in a limited area. The concept of online trajectory generation enables systems to react instantaneously at control level to unforeseen events. Simulation and real-world experimental results carried out on a KUKA Light-Weight Robot arm are presented.
The problems of CNC machine tool (CNCMT) fault diagnosis and production rescheduling have attracted continuous attention because of their great significance to the manufacturing industry. Digital twin is a supporting technology for achieving smart manufacturing and provides a new paradigm for solving these problems. This paper explores a digital twin-driven interaction and cooperation framework and proposes the architecture and implementation mechanism to enable the sharing of data, knowledge, and resource, to realize the fusion of physical space and cyber space, and to improve the accuracy of fault diagnosis. Under this framework, aiming at the influence of CNCMT failure on the initial production planning, a self-adaptation rescheduling method based on Monte Carlo Tree Search (MCTS) algorithm is proposed to provide support for developing more efficient production planning. Finally, the effectiveness of the proposed framework is validated by experimental study. The framework and integrated rescheduling approach can provide guidance for enterprises in implementing CNCMT maintenance and production scheduling to meet high accuracy and reliability requirements.
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