Integrated intelligent green scheduling of sustainable flexible workshop with edge computing considering uncertain machine state

Green scheduling plays an important role in green manufacturing. However, the uncertain interference events, especially sudden changes of machine state, and the improvement of processing quality are ignored in green scheduling. They result in the current green scheduling is one-sided for green manufacturing. Therefore, this paper proposes an integrated method for intelligent green scheduling of the sustainable flexible workshop with edge computing considering uncertain machine state. Firstly, a multi-objective model for green scheduling is built and solved with the makespan, processing cost, processing quality and energy consumption as its optimisation objectives. Then, a hardware system for intelligent monitoring and diagnosis of machine state is established based on wireless sensor network (WSN), edge computing and artificial intelligence (AI). Finally, the diagnosis results of machine state provide feedback to the original green scheduling scheme and the corresponding real-time adjustment called green rescheduling is conducted to response to uncertain dynamic machine state. A case study of intelligent green scheduling where several machines fall into fault operation suddenly is employed to illustrate the practicality and effectiveness of the proposed method, and it is compared with other multi-objective optimisation algorithms for green scheduling. The results indicate that the proposed method is superior to the rivals on this issue. •Edge computing is adopted to green scheduling considering uncertain machine state.•The maximal processing quality is optimized also in green scheduling.•A hybrid-driven mechanism of workshop green rescheduling is put forward.•A composite method for intelligent diagnosis of machine state is proposed.