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

Author

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  • Xiao-Hong Liu

    (Hunan University)

  • Mi-Yuan Shan

    (Hunan University)

  • Li-Hong Zhang

    (Liverpool John Moores University)

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.

Suggested Citation

  • Xiao-Hong Liu & Mi-Yuan Shan & Li-Hong Zhang, 2016. "Low-carbon supply chain resources allocation based on quantum chaos neural network algorithm and learning effect," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(1), pages 389-409, August.
    • Handle: RePEc:spr:nathaz:v:83:y:2016:i:1:d:10.1007_s11069-016-2320-2
    DOI: 10.1007/s11069-016-2320-2
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    References listed on IDEAS

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    1. Choudhary, Alok & Sarkar, Sagar & Settur, Srikar & Tiwari, M.K., 2015. "A carbon market sensitive optimization model for integrated forward–reverse logistics," International Journal of Production Economics, Elsevier, vol. 164(C), pages 433-444.
    2. Nouira, Imen & Hammami, Ramzi & Frein, Yannick & Temponi, Cecilia, 2016. "Design of forward supply chains: Impact of a carbon emissions-sensitive demand," International Journal of Production Economics, Elsevier, vol. 173(C), pages 80-98.
    3. Sazvar, Z. & Mirzapour Al-e-hashem, S.M.J. & Baboli, A. & Akbari Jokar, M.R., 2014. "A bi-objective stochastic programming model for a centralized green supply chain with deteriorating products," International Journal of Production Economics, Elsevier, vol. 150(C), pages 140-154.
    4. Diabat, Ali & Al-Salem, Mohammed, 2015. "An integrated supply chain problem with environmental considerations," International Journal of Production Economics, Elsevier, vol. 164(C), pages 330-338.
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    Cited by:

    1. He, Yuan & Meng, Zhiyi & Xu, Hong & Zou, Yue, 2020. "A dynamic model of evaluating differential automatic method for solving plane problems based on BP neural network algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    2. Shaojian Qu & Yongyi Zhou, 2017. "A Study of The Effect of Demand Uncertainty for Low-Carbon Products Using a Newsvendor Model," IJERPH, MDPI, vol. 14(11), pages 1, October.
    3. Zeguo Qiu & Yunhao Chen & Hao Han & Tianyu Wang, 2024. "Research on Digital Technology to Promote Low-Carbon Transformation of Manufacturing Industries Under the Perspective of Green Credit: An Evolutionary Game Theory Approach," Sustainability, MDPI, vol. 16(24), pages 1-27, December.

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