Digital twin-based job shop anomaly detection and dynamic scheduling

• Scheduling scheme is one of the critical factors affecting the production efficiency. In the actual production, anomalies will lead to scheduling deviation and influence scheme execution, which makes the traditional job shop scheduling methods are not sufficient to meet the needs of real-time and accuracy. By introducing digital twin (DT), further convergence between physical and virtual space can be achieved, which enormously reinforces real-time performance of job shop scheduling. For flexible job shop, an anomaly detection and dynamic scheduling framework based on DT is proposed in this paper. Previously, a multi-level production process monitoring model is proposed to detect anomaly. Then, a real-time optimization strategy of scheduling scheme based on rolling window mechanism is explored to enforce dynamic scheduling optimization. Finally, the improved grey wolf optimization algorithm is introduced to solve the scheduling problem. Under this framework, it is possible to monitor the deviation between the actual processing state and the planned processing state in real time and effectively reduce the deviation. An equipment manufacturing job shop is taken as a case study to illustrate the effectiveness and advantages of the proposed framework.

Ämnesord

SAMHÄLLSVETENSKAP  -- Annan samhällsvetenskap -- Tvärvetenskapliga studier inom samhällsvetenskap (hsv//swe)

SOCIAL SCIENCES  -- Other Social Sciences -- Social Sciences Interdisciplinary (hsv//eng)

Nyckelord

Digital twin (DT)

Multi-level production process monitoring model

Real-time scheduling optimization strategy

Rolling window mechanism

Grey wolf optimization algorithm

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