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首页> 外文期刊>International Journal of Production Research >An integrated neural network-simulation algorithm for performance optimisation of the bi-criteria two-stage assembly flow-shop scheduling problem with stochastic activities
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An integrated neural network-simulation algorithm for performance optimisation of the bi-criteria two-stage assembly flow-shop scheduling problem with stochastic activities

机译：具有随机活动的双标准两阶段装配流水车间调度问题性能优化的集成神经网络仿真算法

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摘要

This paper presents an integrated computer simulation and Artificial Neural Network (ANN) algorithm for a stochastic Two-Stage Assembly Flow-Shop Scheduling Problem (TSAFSP) with setup times under a weighted sum of makespan and mean completion time (MCT) criteria, known as bi-criteria. Significantly, it should be noted that there is no mathematical model to analyse the stochastic model, therefore simulation is used to solve the problem. The simulation model enables decision makers to consider the influence of job scheduling on machines in order to examine both criteria simultaneously. Since it is not possible to evaluate all sequence combinations using the simulation model in a reasonable time, multilayered neural network meta-models have been trained and used to estimate objective function values composed of both makespan and mean completion time criteria for the stochastic TSAFSP. To the best of the authors' knowledge, this is the first study that considers stochastic machine breakdown, processing times, setup times, makespan and mean completion time as objectives concurrently. The TSAFSP is modelled by Visual SLAM simulation software. The simulation output results are thengiven to the ANN as inputs to build the meta-model. This meta-model is then used to obtain the results withIhe optimum values. The advantage of these meta-model applications is a reduction in the number of simulation runs and consequently a reduced run time. Also, this is the first study that introduces an intelligent and flexible algorithm for handling stochastic TSAFSP.
机译：本文提出了一种集成的计算机仿真和人工神经网络（ANN）算法，用于求解带有制造时间和平均完成时间（MCT）加权总和的建立时间的随机两阶段装配流水车间调度问题（TSAFSP）。双重标准。值得注意的是，由于没有数学模型可以分析随机模型，因此可以通过仿真来解决该问题。仿真模型使决策者可以考虑作业调度对机器的影响，以便同时检查这两个标准。由于不可能在合理的时间内使用仿真模型评估所有序列组合，因此已经训练了多层神经网络元模型，并将其用于估计由随机TSAFSP的makepan和平均完成时间标准组成的目标函数值。据作者所知，这是同时考虑随机机器故障，处理时间，设置时间，制造时间和平均完成时间的第一项研究。 TSAFSP由Visual SLAM仿真软件建模。然后将模拟输出结果提供给ANN作为输入以构建元模型。然后，该元模型用于获得具有最佳值的结果。这些元模型应用程序的优点是减少了模拟运行的次数，因此减少了运行时间。此外，这是第一个介绍用于处理随机TSAFSP的智能灵活算法的研究。

著录项

  • 来源
    《International Journal of Production Research》 |2012年第24期|7271-7284|共14页
  • 作者

    A. Azadeh; M. Jeihoonian; B. Maleki Shoja; S.H. Seyedmahmoudi;

  • 作者单位

    Department of Industrial Engineering and Center of Excellence for Intelligent-based Experimental Mechanics, University College of Engineering, University of Tehran, Tehran, Iran;

    Department of Industrial Engineering and Center of Excellence for Intelligent-based Experimental Mechanics, University College of Engineering, University of Tehran, Tehran, Iran;

    Department of Industrial Engineering and Center of Excellence for Intelligent-based Experimental Mechanics, University College of Engineering, University of Tehran, Tehran, Iran;

    Department of Industrial Engineering and Center of Excellence for Intelligent-based Experimental Mechanics, University College of Engineering, University of Tehran, Tehran, Iran;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    simulation applications; scheduling;

    机译：模拟应用;排程;

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