An adaptive digital twin framework for flow shop scheduling and production planning with machine learning integration
提出一个闭环数字孪生框架，结合支持向量机预测需求和关联规则挖掘预测停机，实时优化流水车间的生产计划与调度，降低运营成本13%。
Demand fluctuations, machine downtime, and execution plans’ uncertainties affect production planning and scheduling in flow shops; therefore, these factors must be considered during planning to prevent operational disruptions. Unlike existing static integrated planning and scheduling models or heuristic-based dynamic approaches, this work introduces a closed-loop Digital Twin (DT) framework that preserves the mathematical optimality of a Mixed-Integer Nonlinear Programming (MINLP) model while enabling real-time adaptability. Our primary innovation is the systematic and real-time integration of Support Vector Machine (SVM)-based demand forecasting and Association Rule Mining (ARM)-based proactive downtime prediction. We develop a data-driven DT that synchronises the MINLP with live shop-floor data. The framework integrates SVM for demand forecasting and ARM for proactive machine downtime prediction. Through event-driven re-optimisation, the DT continuously updates key parameters and triggers schedule revisions in response to operational deviations. Computational experiments demonstrate that the proposed approach reduces total operational costs by 13%, significantly decreases machine waiting times and unscheduled downtime, and improves schedule stability compared to static and data-driven baselines. The results underscore the unique value of closed-loop adaptive systems in enhancing resilience and efficiency. This study bridges the gap between traditional optimisation models and real-world execution to offer more responsive and robust production systems.