Digital twin data-driven proactive job-shop scheduling strategy towards asymmetric manufacturing execution decision

Abstract

Abstract The information asymmetry phenomenon widely exists in production management decisions due to the latency of manufacturing data transmissions. Also, stochastic events on the physical production site will result in information asymmetry, which may lead to inconsistency between current execution and previous resource allocation plans. It is meaningful and important for developing an information model based on the Internet of Manufacturing Things to timely and actively adjust the scheduling strategy to meet the symmetry requirements of the production execution process. Based on the digital twin data collected from the workshop, a proactive job-shop scheduling strategy was discussed in this paper. Firstly, the mechanism for the influence of delayed local operations on makespan was deduced. Then, a framework for implementing the proactive job-shop scheduling strategy was proposed. Coordination point was used to determine the adjustment interval of local operations; right-shift rule with delay time constraints was used to adjust the unprocessed operation sequences on machines. Finally, the examples including 6*6 (6 jobs, 6 machines) and 20*40 (20 jobs, 40 machines) were presented to verify the effectiveness and scalability of the proposed method. It can be predicted that the proactive scheduling strategy provides the online decisions for the efficient and smooth execution of the digital twin-driven workshop production.