Low-carbon supply chain resources allocation based on quantum chaos neural network algorithm and learning effect

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• 作者：Xiao-Hong Liu ; Mi-Yuan Shan ; Li-Hong Zhang
• 刊名：Natural Hazards
• 出版年：2016
• 出版时间：August 2016
• 年：2016
• 卷：83
• 期：1
• 页码：389-409
• 全文大小：2,750 KB
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Hydrogeology
  Geophysics and Geodesy
  Geotechnical Engineering
  Civil Engineering
  Environmental Management
  
• 出版者：Springer Netherlands
• ISSN：1573-0840
• 卷排序：83

文摘

This paper focuses on designing a novel quantum chaos neural network algorithm for low-carbon supply chain resources allocation problem (LCSCRAP) which is an efficient extension of the resources allocation. Quantum chaos neural network algorithm based on cloud model (C-QCNNA) is put forward to solve the LCSCRAP with several conflicting and incommensurable multi-objectives. The results of simulation experiments have been obtained from the set of standard instances, and the C-QCNNA is confirmed to be very competitive after extensive experiments. The computational results have proved that the C-QCNNA is an efficient and it is effective for the LCSCRAP. This study can not only develop the C-QCNNA for the LCSCRAP, but also promote the C-QCNNA and cloud model theory themselves. Simultaneously, it has important theoretical and practical significance.KeywordsLow-carbon supply chainQuantum chaos neural network algorithmLearning effectCloud model