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

www.nature.com/scientificreports OPEN Digital twin data‑driven proactive job‑shop scheduling strategy towards asymmetric manufacturing execution decision Fuqiang Zhang1,2, Junyan Bai1,2, Dongyu Yang3* & Qiang Wang3 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. Due to the latency of manufacturing data transmissions and stochastic events on the physical production site, the information asymmetry phenomenon has widely existed in the production management decision process, which may lead to inconsistency between current execution and previous resource allocation plans. Proactive scheduling is an important part of maintaining the symmetry decision in workshops. Fierce market competition, personalized customer demand, and service-oriented production have prompted small and medium-sized enterprises to switch to a multi-variety, small-batch flexible production m odel1,2. In this case, enterprises must enhance their scheduling capabilities to shorten the product delivery cycles, increase equipment utilization, and reduce production costs to obtain greater benefits. Focusing on the traditional job-shop scheduling problems, the processing time for different jobs on the machine was estimated based on simulation software or practical experience. However, the uncertainty factors, such as the shortage of AGVs (Automated Guided Vehicles) or incorrect operation of workers, can lead to the actual processing time being inconsistent with the estimated theoretical processing time. In this situation, how to adjust the strategy in time and actively maintain the symmetry of the digital twin workshop is very important. It must be pointed out that the delay in makespan is an important macro index to evaluate the symmetry of the digital twin workshop. The delay in makespan R can be measured by the maximum completion time delay rate, which can be formulated as: R= ms − ms0 × 100% ms0 (1) where ms denotes the actual makespan; ms0 denotes the theoretical makespan. With the Internet of Manufacturing Things (IoMT) development, all kinds of sensors are used to monitor and control the production process in a digital twin workshop3–6. Among them, RFID (Radio Frequency 1 Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, Shaanxi, China. 2Institute of Smart Manufacturing Systems Engineering, Chang’an University, Xi’an 710064, Shaanxi, China. 3China Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou 510610, China. *email: Scientific Reports | (2022) 12:1546 | https://doi.org/10.1038/s41598-022-05304-w 1 Vol.:(0123456789) www.nature.com/scientificreports/ Identification) technology for object recognition and tracking is the most mature technology in IoMT applications. Numerous industrial practices show that IoMT can provide us with the real-time tracking of production processes that suffer from unpredictable and hidden interference. In addition to these real-time on-site tracking information, the digital twin data are more extensive, including process data, such as process route planning; some predictive data, such as the processing time of unprocessed operations predicted by big data technology7. In the context of digital twin data, scheduling strategies must be determined to make the production run smoothly. Based on the Gantt chart and digital twin data, a proactive job-shop scheduling strategy was discussed in this paper. When there are uncertain abnormal events, proactive scheduling can update or reschedule the initial scheme to minimize the impact of various interference factors on production performance. The main contributions of this paper are as follows: • The definitions of critical path and critical operations were given. The mechanism for the influence of delayed local operations on the makespan was deduced. • To maintain the initial scheduling scheme, the local adjustment rules for critical operations and non-critical operations were proposed. • The case verifies the implementation framework of proactive scheduling, and the results show that the delay in makespan can be reduced to a certain extent. The rest of this paper is organized as follows. “Brief review” section presents a brief review of proactive jobshop scheduling. Local operation delay impact on makespan is discussed in “Local operations delay impact on makespan” section. “Proactive scheduling strategy” section shows the proactive scheduling strategy and its implementation framework. In “Case study” section, the case study is presented. Conclusions are drawn in “Conclusion” section. Brief review As a typical NP-hard combinatorial optimization problem, job-shop scheduling has always been a research hotspot in academia and industry8. Nowadays, industries are seeking models and methods that are not only able to provide efficient overall production performance, but also for reactive systems facing a growing set of unpredicted events9. The researchers have primarily focused on the stochastic resource-constrained scheduling problem. For example, Ghezail proposed a graphical representation of robustness to assist the decision-maker in understanding the consequences of possible p erturbations10. Ryu incorporated the uncertainty in processing times and equipment availabilities into scheduling models, which were then transformed to multiparametric mixed-integer linear programming (mp-MILP) problems11. Jiang established a complex manufacturing network and a dynamic scheduling algorithm based on multi-layer network metrics were used to solve multi-resources and independent-tas (...truncated)