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首页> 外文会议>ICIHDS 2007;International conference on impulsive and hybrid dynamical systems >A New Model to Evaluating the Performance of Third Party Logistics Enterprises based on Balanced Scorecard and Artificial Neural Network Supported by Particle Swarm Optimization Algorithm
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A New Model to Evaluating the Performance of Third Party Logistics Enterprises based on Balanced Scorecard and Artificial Neural Network Supported by Particle Swarm Optimization Algorithm

机译：基于粒子群优化算法的平衡计分卡和人工神经网络的第三方物流企业绩效评价新模型。

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摘要

The need for performance measurement systems is imminent in the logistics enterprises.This paper proposes a new model to evaluating the performance of third party logistics(TPL) enterprises based on balanced scorecard and artificial neural network(ANN). A novel hybrid particle swarm optimization(HPSO)-based learning method is utilized in the ANN.The reliability of the ANN model is tested by a practical example. The results show that the ANN model learned by HPSO algorithm has relative high accuracy and is better than the widely-used back propagation algorithm.The empirical findings suggest that the ANNs model can be a persuasive analytical tool for the performance evaluation of the TPL enterprises.
机译：物流企业迫切需要绩效评估系统。本文提出了一种基于平衡计分卡和人工神经网络的第三方物流企业绩效评估模型。在人工神经网络中采用了一种基于混合粒子群优化（HPSO）的新型学习方法。通过实例验证了人工神经网络模型的可靠性。结果表明，采用HPSO算法学习的人工神经网络模型具有较高的准确性，优于广泛使用的反向传播算法。实证结果表明，人工神经网络模型可以作为TPL企业绩效评价的有说服力的分析工具。

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