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

Xiao-Hong Liu, Mi-Yuan Shan () and Li-Hong Zhang
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Xiao-Hong Liu: Hunan University
Mi-Yuan Shan: Hunan University
Li-Hong Zhang: Liverpool John Moores University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2016, vol. 83, issue 1, No 19, 389-409

Abstract: Abstract 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.

Keywords: Low-carbon supply chain; Quantum chaos neural network algorithm; Learning effect; Cloud model (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11069-016-2320-2

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