Human-Machine Cooperation Based Adaptive Scheduling for a Smart Shop Floor

Abstract

With the increasing demand of personalized products and the application of emerging technologies, substantial unexpected events appears in smart factories. Machine learning based adaptive scheduling shows significant appeal in smart shop floors, yet still has limitations in accommodating unexpected events. This paper presents a novel framework of HCPS (Human Cyber Physical System) based on the conventional CPS. A human-machine cooperative mechanism is proposed to coordinate task allocation between human and machine. Meanwhile, in order to integrate human intelligence and machine intelligence within scheduling decision making, a novel human-machine cooperative approach for adaptive scheduling is put forward. In the process of online scheduling, human operators adjust the deviation of production indicators on the basis of current condition. Subsequently, an enhanced fuzzy inference system combining with human intelligence is designed to obtain optimal dispatching rules, in which parameters are reduced by a K-means algorithm and optimized by a PSO algorithm. Finally, a case study is performed on the Minifab model. The simulation results validate the superiority of the proposed framework and approaches, and show good potential in efficiency and stability.