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首页> 外文期刊>International Journal of Production Research >Using Ensemble And Metaheuristics Learning Principles With Artificial Neural Networks To Improve Due Date Prediction Performance
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Using Ensemble And Metaheuristics Learning Principles With Artificial Neural Networks To Improve Due Date Prediction Performance

机译：将集成和元启发式学习原理与人工神经网络结合使用以提高到期日预测性能

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摘要

One of the common and important problems in production scheduling is to quote an attractive but attainable due date for an arriving customer order. Among a wide variety of prediction methods proposed to improve due date quotation (DDQ) accuracy, artificial neural networks (ANN) are considered the most effective because of their flexible non-linear and interaction effects modelling capability. In spite of this growing use of ANNs in a DDQ context, ANNs have several intrinsic shortcomings such as instability, bias and variance problems that undermine their accuracy. In this paper, we develop an enhanced ANN-based DDQ model using machine learning, evolutionary and metaheuristics learning concepts. Computational experiments suggest that the proposed model outperforms the conventional ANN-based DDQ method under different shop environments and different training data sizes.
机译：生产调度中的常见和重要问题之一是为到达的客户订单报价一个有吸引力的但可以达到的到期日期。在各种旨在提高到期日报价（DDQ）准确性的预测方法中，人工神经网络（ANN）因其灵活的非线性和交互作用建模能力而被认为是最有效的。尽管在DDQ上下文中ANN的使用越来越多，但ANN仍存在一些固有的缺点，例如不稳定，偏差和方差问题，这会破坏其准确性。在本文中，我们使用机器学习，进化和元启发式学习概念开发了基于ANN的增强型DDQ模型。计算实验表明，在不同的车间环境和不同的训练数据量下，该模型优于基于ANN的传统DDQ方法。

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