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Discovery of Key Production Nodes in Multi-objective Job Shop Based on Entropy Weight Fuzzy Comprehensive Evaluation

机译:基于熵权模糊综合评价的多目标作业商店关键生产节点发现

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摘要

The multi-objective Job Shop complex network model based on data information is a new idea to solve the transformation of multi-objective shop scheduling problem in recent years. Finding key nodes on the complex networks model is the focus of this paper. The existing key nodes recognition method ignores the overall characteristics of the network, is susceptible to subjective factors, and does not apply to data based complex networks model. According to the characteristics of subjective and objective weighting, the entropy weight method in fuzzy mathematics is applied to the method of analytic hierarchy process (AHP). The next step is to establish a key nodes recognition method suitable for new model-Entropy weight fuzzy comprehensive evaluation method. To some extent, this method has made up for the lack of subjectivity and index capability of the method of analytic hierarchy process. Finally, the simulation results show that the method can effectively mine the key nodes in the model, and prove the rationality and effectiveness of the method.
机译:基于数据信息的多目标作业商店复杂网络模型是解决近年来解决多目标商店调度问题的新主意。在复杂网络模型上查找关键节点是本文的焦点。现有关键节点识别方法忽略了网络的整体特征,易受主观因素的影响,并且不适用于基于数据的复杂网络模型。根据主观和客观加权的特征,模糊数学中的熵权法应用于分析层次过程(AHP)的方法。下一步是建立一个适用于新模型 - 熵权重模糊综合评估方法的关键节点识别方法。在某种程度上,这种方法已经弥补了分析层次方法方法的缺乏主观性和指标能力。最后,仿真结果表明,该方法可以有效地挖掘模型中的关键节点,并证明该方法的合理性和有效性。

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