基于退化机器模型的分布式柔性生产系统:性能分析、任务调度及预测性维护
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作者单位:

北京理工大学

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中图分类号:

TP273

基金项目:

北京市自然科学基金项目,中国国家自然科学基金项目,国家重点基础研究发展计划(973计划)


Distributed flexible systems with degenerate machines: performance analysis, production scheduling, and predictive maintenance
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Affiliation:

Beijing Institute of Technology

Fund Project:

Beijing Municipal Natural Science Foundation,National Natural Science Foundation of China,The National Basic Research Program of China (973 Program)

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    摘要:

    在近些年的制造环境中,由于市场对多品种、小批量定制产品需求的增加,生产制造更加深入地向着柔性方向发展.为更好地利用现有资源,提高生产效率,实时性能评估与预测、基于小批量生产的实时调度以及优化改进等在分布式柔性生产系统中具有重要的研究意义.本文主要研究基于退化机器模型的多批次串行生产线的性能分析问题,并对分布式生产系统进行任务调度及预测性维护.具体地说,对于具有退化机器模型及有限容量缓冲区的生产系统,首先,采用马尔科夫分析方法建立数学模型.然后,提出精确方法来计算该生产系统的实时性能指标,并提出一种针对模型的遗传算法实现调度问题的求解方案.此外,提出基于退化机器模型的预测性维护策略以减少完成时间.最后,通过数值实验验证了该算法的可行性和有效性.

    Abstract:

    In the modern manufacturing environment, the production systems develop towards a flexible direction due to increasing of the market demands of multiple varieties and small batch-based customized products. In order to make better use of existing resources and improve production efficiency, real-time performance evaluation and prediction of real-time scheduling and optimization improvement based on small batch production have important research significance in distributed flexible production system. This paper mainly studies the performance analysis of multi-batch serial production lines based on degenerate machines, and carries out task scheduling and predictive maintenance for distributed production system. Specifically, for the machines with degradation models and the production system with limited buffer capacity, the mathematical model is firstly established by Markov analysis method. Then, an accurate analysis method is proposed to calculate the real-time performance indicators of the evaluation system, and a genetic algorithm based on the model is proposed to solve the scheduling problem. In addition, a predictive maintenance strategy is proposed to reduce the completion time for machines with degradation models. Finally, the feasibility and effectiveness of the mathematical model and algorithm are illustrated by numerical experiments.

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历史
  • 收稿日期:2021-12-10
  • 最后修改日期:2022-04-08
  • 录用日期:2022-04-08
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