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

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...

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Bibliographic Details
Published in:Natural hazards (Dordrecht) 2016-08, Vol.83 (1), p.389-409
Main Authors: Liu, Xiao-Hong, Shan, Mi-Yuan, Zhang, Li-Hong
Format: Article
Language:English
Subjects:
Algorithms
Allocations
Carbon
Chaos theory
Civil Engineering
Computer simulation
Earth and Environmental Science
Earth Sciences
Environmental Management
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Learning
Natural Hazards
Neural networks
Original Paper
Quantum theory
Resource allocation
Supply chains
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Summary: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.
ISSN:0921-030X
1573-0840
DOI:10.1007/s11069-016-2320-2