Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (8): 2340-2345.DOI: 10.11772/j.issn.1001-9081.2016.08.2340

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Dynamic shop scheduling problem of maintenance point prediction

KUANG Peng1, WU Jinzhao1,2   

  1. 1. Institute of Computer Application, Chinese Academy of Sciences, Chengdu Sichuan 610041, China;
    2. Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis(Guangxi University for Nationalities), Nanning Guangxi 530006, China
  • Received:2016-01-13 Revised:2016-03-06 Online:2016-08-10 Published:2016-08-10
  • Supported by:
    This work is partially supported by the Natural Science Foundation of China (11371003, 11461006), the Natural Science Foundation of Guangxi (2012GXNSFGA060003), the Scientific Research Fund of Guangxi Education Department (201012MS274).


匡鹏1, 吴尽昭1,2   

  1. 1. 中国科学院 成都计算机应用研究所, 成都 610041;
    2. 广西混杂计算与集成电路设计分析重点实验室(广西民族大学), 南宁 530006
  • 通讯作者: 吴尽昭
  • 作者简介:匡鹏(1990-),男,湖北荆州人,硕士研究生,主要研究方向:复杂系统的形式化分析与验证;吴尽昭(1965-),男,吉林扶余人,研究员,博士,主要研究方向:形式刻画与验证、集成电路设计与验证。
  • 基金资助:

Abstract: Aiming at the uncertain issue of the production plan in manufacturing industry, an optimal scheduling method which combined the prediction of maintenance point with the adaptive algorithm of genetic and simulated annealing was proposed. First of all, the Auto Regressive Integrated Moving Average model (ARIMA) was used to predict equipment failure rate; then the Weibull distributed model was used to reverse the equipment maintenance point in the future by equipment failure rate; finally, regarding the maintenance point as a constraint condition, the traditional production scheduling problem was solved by the adaptive hybrid algorithm of genetic and simulated annealing. The random scheduling situation of equipment for maintenance was analyzed in combination with the practical situation of the factory, and to determine the optimal scheduling scheme, the minimum makespan was regarded as a goal to obtain the scheduling plan of each task and maintenance point of each equipment. Experimental results show that the adaptive genetic and simulated annealing algorithm has good performance. In the production workshop of a certain factory in Hebei, the average failure rate of the equipment which used optimization scheduling method was relatively reduced by 3.46 percent than that before optimization.

Key words: AutoRegressive Integrated Moving Average model (ARIMA), equipment malfunction rate, Genetic Algorithm (GA), Simulated Annealing (SA)algorithm, production scheduling

摘要: 针对制造业中生产计划的不确定问题,提出一种维修时点预测与自适应的遗传模拟退火算法相结合的优化调度方法。该方法首先利用差分自回归移动平均模型预测设备未来的故障率,然后借助电气设备的威布尔(Weibull)分布模型逆向求出设备未来故障发生时刻,最后将此作为约束条件,利用自适应的遗传模拟退火算法解决传统的生产调度问题。结合工厂实际情况,主要分析了设备有无维修的随机调度问题,以最小化最大完工时间为目标,获取每一个任务的调度计划以及每一台设备的维修时点,确定出最佳调度方案。实验表明自适应的遗传模拟退火算法的性能较好。在河北某工厂的生产车间中,设备在运行调度方法后三个月的平均故障率比运行前相对降低了3.46%。

关键词: 自回归移动平均模型, 设备故障率, 遗传算法, 模拟退火算法, 生产调度

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