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Control and Simulation Center, Harbin Institute of Technology, 150001 Harbin, China

机译:哈尔滨工业大学控制与仿真中心,150001哈尔滨,中国

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This paper presents a multicriteria DRC scheduler in order to select appropriate dispatching rules. This scheduler integrates several tools, namely; a simulation model, a backpropagation neural network (BPNN) and a Multicriteria decision aid (MCDA) method. Simulation is used to collect predefined performance measures corresponding to decision rule set and system state variables. Because of the time consuming nature of simulation, BPNN is used to obtain the performance measures for each alternative schedule. In order to compare the system performance between all alternatives, the evaluation of each alternative is performed by PROMETHEE, which is a well-known MCDA method. By means of a realistic numerical example, the proposed methodology is proved to be an effective method in a DRC manufacturing system.
机译:本文介绍了多轨道DRC调度程序,以便选择适当的调度规则。此调度程序集成了多个工具,即;仿真模型,反向化神经网络(BPNN)和多铁路决策辅助方法(MCDA)方法。模拟用于收集与决策规则集和系统状态变量对应的预定义的性能测量。由于仿真的耗时性,BPNN用于获得每个替代计划的性能措施。为了比较所有替代方案之间的系统性能,通过PROMETHEE执行每个替代方案的评估,这是一个众所周知的MCDA方法。借助于现实的数值示例,所提出的方法被证明是DRC制造系统中的有效方法。

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