IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v83y2016i1d10.1007_s11069-016-2320-2.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-016-2320-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-016-2320-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Fahimnia, Behnam & Sarkis, Joseph & Eshragh, Ali, 2015. "A tradeoff model for green supply chain planning:A leanness-versus-greenness analysis," Omega, Elsevier, vol. 54(C), pages 173-190.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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 1-24, October.
    2. 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).
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Waltho, Cynthia & Elhedhli, Samir & Gzara, Fatma, 2019. "Green supply chain network design: A review focused on policy adoption and emission quantification," International Journal of Production Economics, Elsevier, vol. 208(C), pages 305-318.
    2. Ameknassi, Lhoussaine & Aït-Kadi, Daoud & Rezg, Nidhal, 2016. "Integration of logistics outsourcing decisions in a green supply chain design: A stochastic multi-objective multi-period multi-product programming model," International Journal of Production Economics, Elsevier, vol. 182(C), pages 165-184.
    3. Zhalechian, M. & Tavakkoli-Moghaddam, R. & Zahiri, B. & Mohammadi, M., 2016. "Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 89(C), pages 182-214.
    4. Alzaman, Chaher & Zhang, Zhi-Hai & Diabat, Ali, 2018. "Supply chain network design with direct and indirect production costs: Hybrid gradient and local search based heuristics," International Journal of Production Economics, Elsevier, vol. 203(C), pages 203-215.
    5. Tajbakhsh, Alireza & Hassini, Elkafi, 2022. "A game-theoretic approach for pollution control initiatives," International Journal of Production Economics, Elsevier, vol. 254(C).
    6. Yi Liao & Ali Diabat & Chaher Alzaman & Yiqiang Zhang, 2020. "Modeling and heuristics for production time crashing in supply chain network design," Annals of Operations Research, Springer, vol. 288(1), pages 331-361, May.
    7. Ozgur Kabadurmus & Mehmet S. Erdogan, 2020. "Sustainable, multimodal and reliable supply chain design," Annals of Operations Research, Springer, vol. 292(1), pages 47-70, September.
    8. Sazvar, Zeinab & Sepehri, Mehran, 2020. "An integrated replenishment-recruitment policy in a sustainable retailing system for deteriorating products," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    9. Gao, Jingzhe & Xiao, Zhongdong & Cao, Binbin & Chai, Qiangfei, 2018. "Green supply chain planning considering consumer’s transportation process," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 311-330.
    10. Katherinne Salas-Navarro & Paula Serrano-Pájaro & Holman Ospina-Mateus & Ronald Zamora-Musa, 2022. "Inventory Models in a Sustainable Supply Chain: A Bibliometric Analysis," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
    11. Kumar, V.N.S.A. & Kumar, V. & Brady, M. & Garza-Reyes, Jose Arturo & Simpson, M., 2017. "Resolving forward-reverse logistics multi-period model using evolutionary algorithms," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 458-469.
    12. Herrera Rodriguez, Manuel & Agrell, Per J. & Manrique-de-Lara-Peñate, Casiano & Trujillo, Lourdes, 2022. "A multi-criteria fleet deployment model for cost, time and environmental impact," International Journal of Production Economics, Elsevier, vol. 243(C).
    13. Ji, Jingna & Li, Tao & Yang, Lei, 2023. "Pricing and carbon reduction strategies for vertically differentiated firms under Cap-and-Trade regulation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    14. Shen, Jiayu, 2020. "An environmental supply chain network under uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    15. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    16. A. Muñoz-Villamizar & J. Santos & P. Grau & E. Viles, 2021. "Toolkit for simultaneously improving production and environmental efficiencies," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(4), pages 1219-1230, December.
    17. Behnam Fahimnia & Joseph Sarkis & Angappa Gunasekaran & Reza Farahani, 2017. "Decision models for sustainable supply chain design and management," Annals of Operations Research, Springer, vol. 250(2), pages 277-278, March.
    18. Ali Heidari & Din Mohammad Imani & Mohammad Khalilzadeh & Mahdieh Sarbazvatan, 2023. "Green two-echelon closed and open location-routing problem: application of NSGA-II and MOGWO metaheuristic approaches," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 9163-9199, September.
    19. Rahimi, Mohammad & Baboli, Armand & Rekik, Yacine, 2017. "Multi-objective inventory routing problem: A stochastic model to consider profit, service level and green criteria," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 101(C), pages 59-83.
    20. Abolfazl Gharaei & Alireza Amjadian & Ali Shavandi & Amir Amjadian, 2023. "An augmented Lagrangian approach with general constraints to solve nonlinear models of the large-scale reliable inventory systems," Journal of Combinatorial Optimization, Springer, vol. 45(2), pages 1-37, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:83:y:2016:i:1:d:10.1007_s11069-016-2320-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.