Human–Machine Collaborative Decision-Making Method Based on Confidence for Smart Workshop Dynamic Scheduling
Dongyuan Wang, Fei Qiao, Liuen Guan, Juan Liu, Chen Ding
Abstract
Dynamic scheduling is one of the most important problems in the field of production scheduling. Existing ways to solve the problem are mainly based on experienced workers or automatic scheduling models (SMs). Because of the complementary advantages of workers and SMs, their combination has the potential to be a better solution. In this letter, a human-machine collaborative decision-making method based on confidence (HMCDM/C) is proposed. SMs are in charge of the automatic generation of decisions, and workers have the power to revise unreliable decisions. Furthermore, a threshold-based handover mechanism is proposed to determine when workers are involved in decision-making. Firstly, a measurement method is developed to quantify the confidence level of SM decisions. Secondly, an evaluation method is presented to determine the threshold of confidence levels, which can be used to discriminate whether the SM decisions are acceptable or not at a certain confidence level. Finally, several experiments are conducted in a smart workshop. The results show that the HMCDM/C can effectively coordinate workers with different experience levels and SMs, and has a very competitive performance.