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With the development of industrial intelligence and information technology, the industrial network presents a new characteristic of multivariate network. In this paper, a production element scheduling optimisation algorithm for dynamic change of production cost in multivariate industrial network is designed in order to reduce the scheduling element falling into local optimum. First of all, the multi -time granularity prediction method based on stability selection predicts the production cost of components in multivariate industrial networks. Subsequently, multi -dimensional training is based on multi-dimensional training to learn scheduling optimization algorithm to solve the scheduling optimization of components. Finally, the experiment shows that the algorithm has better performance for optimization of task scheduling in multivariate industrial networks.

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Optimizing Production Component Scheduling in Multivariate Industrial Networks with Dynamic Changes in Production Costs

  • 会议论文

  • 发表日期：

    2025年

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  • Xiangxiang Xing,
  • Fulin Chen,
  • Tianyu Zuo,
  • Pan Li,
  • Kai Di,
  • Xin Wang,
  • Lifeng Chen,
  • Yichuan Jiang,
  • Dan Chen

摘要

With the development of industrial intelligence and information technology, the industrial network presents a new characteristic of multivariate network. In this paper, a production element scheduling optimisation algorithm for dynamic change of production cost in multivariate industrial network is designed in order to reduce the scheduling element falling into local optimum. First of all, the multi -time granularity prediction method based on stability selection predicts the production cost of components in multivariate industrial networks. Subsequently, multi -dimensional training is based on multi-dimensional training to learn scheduling optimization algorithm to solve the scheduling optimization of components. Finally, the experiment shows that the algorithm has better performance for optimization of task scheduling in multivariate industrial networks.