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Energy-efficient operation of manufacturing systems is critical for industrial enterprises in current environmentally conscious society. Decreasing the idle time of a machine is one of the main methods to achieve energy-efficient production. From the system level, when and how long a machine can be turned into standby state with lower energy consumption is still a difficult problem for unreliable manufacturing systems considering less throughput loss. In this paper, a novel multi-state decision method based on fuzzy logic is proposed to switch a machine into different sleep states considering real-time work in process inventory of buffers. Three basic modules and their corresponding fuzzy controllers are presented to construct complex manufacturing systems with disassembly and assembly workstations. The fuzzy rules for machine state decision are generated based on the expert/production knowledge. By means of simulation experiments, the effectiveness of the proposed method is illustrated for an unreliable complex manufacturing system.

The energy efficient operation of a manufacturing system is important for sustainable development of industry. Apart from the device and process level, energy saving methods at the system level has attracted increasing attention with the rapid growth of the industrial Internet of things technology, which makes it possible to sense and collect real-time data from the production line and provide more opportunities for online control for energy saving purposes. In this paper, a dynamic adaptive fuzzy reasoning Petri net is proposed to decide the machine energy saving state considering the production information of a discrete stochastic manufacturing system. Fuzzy knowledge for energy saving operations of a machine is represented in weighted fuzzy production rules with certain values. The rules describe uncertain, imprecise, and ambiguous knowledge of machine state decisions. This makes an energy saving sleep decision in advance when a machine has the inclination of starvation or blockage, which is based on the real-time production rates and level of connected buffers. A dynamic adaptive fuzzy reasoning Petri net is formally defined to implement the reasoning process of the machine state decision. A manufacturing system case is used to demonstrate the application of our method and the results indicate its effectiveness for energy saving operation purposes.