IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v12y2025i1d10.1057_s41599-025-04505-8.html
   My bibliography  Save this article

Logistics demand prediction using fuzzy support vector regression machine based on Adam optimization

Author

Listed:
  • Jing Quan

    (Chongqing University of Technology)

  • Yiwen Peng

    (Chongqing University of Technology)

  • Liyun Su

    (Chongqing University of Technology)

Abstract

As the digital economy experiences swift advancements, demand prediction in logistics holds a crucial significance for firms operating in the logistics sector. The primary aim of this research paper is to ascertain the optimal method for forecasting logistics demand based on the logistics demand data from the Chengdu-Chongqing Dual-City Economic Circle (CC-DEC). The importance and widespread application of machine learning technologies in intelligent forecasting are undeniable. Specifically in Logistics Demand Prediction, Support Vector Regression plays a pivotal role in enhancing accuracy. Precise prediction of logistics demand is essential for optimizing resource allocation efficiency, which is the core focus of this research endeavor. In this study, we conduct the Fuzzy Support Vector Regression Machine approach based on Adam optimization (FSVR-AD). Then we have developed a comprehensive Logistics Demand Prediction index system tailored for the CC-DEC in China, particularly focusing on the dimensions of carbon neutrality and carbon peaking. Three distinct forecasting models are constructed on historical data spanning from 2005 to 2021, aiming to accurately predict the logistics demand within the economic circle. Our analysis reveals that all three models exhibit high predictive accuracy. However, the FSVR-AD demonstrates superior performance, as its predictions align more closely with actual values, resulting in reduced error margins. Given its accuracy and precision, the FSVR-AD is an ideal choice for constructing logistic demand forecasts. Its predictions offer a reliable reference for strategic planning in logistics management, enabling companies to optimize automation and innovate supply chain processes to align with evolving trends.

Suggested Citation

  • Jing Quan & Yiwen Peng & Liyun Su, 2025. "Logistics demand prediction using fuzzy support vector regression machine based on Adam optimization," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-13, December.
    • Handle: RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-04505-8
    DOI: 10.1057/s41599-025-04505-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-025-04505-8
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-025-04505-8?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. Hao, Yu & Zhang, Zong-Yong & Liao, Hua & Wei, Yi-Ming, 2015. "China’s farewell to coal: A forecast of coal consumption through 2020," Energy Policy, Elsevier, vol. 86(C), pages 444-455.
    2. Lin, Jiang & Fridley, David & Lu, Hongyou & Price, Lynn & Zhou, Nan, 2018. "Has coal use peaked in China: Near-term trends in China's coal consumption," Energy Policy, Elsevier, vol. 123(C), pages 208-214.
    3. Ya Li & Zhanguo Wei, 2022. "Regional Logistics Demand Prediction: A Long Short-Term Memory Network Method," Sustainability, MDPI, vol. 14(20), pages 1-17, October.
    4. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    Full references (including those not matched with items on IDEAS)

    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. Qiao, Hui & Chen, Siyu & Dong, Xiucheng & Dong, Kangyin, 2019. "Has China's coal consumption actually reached its peak? National and regional analysis considering cross-sectional dependence and heterogeneity," Energy Economics, Elsevier, vol. 84(C).
    2. Xuguang Hao & Mei Song & Yunan Feng & Wen Zhang, 2019. "De-Capacity Policy Effect on China’s Coal Industry," Energies, MDPI, vol. 12(12), pages 1-16, June.
    3. Jia, Zong-qian & Zhou, Zhi-fang & Zhang, Hong-jie & Li, Bo & Zhang, You-xian, 2020. "Forecast of coal consumption in Gansu Province based on Grey-Markov chain model," Energy, Elsevier, vol. 199(C).
    4. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    5. Abdul Rehman & Hengyun Ma & Magdalena Radulescu & Crenguta Ileana Sinisi & Zahid Yousaf, 2021. "Energy Crisis in Pakistan and Economic Progress: Decoupling the Impact of Coal Energy Consumption in Power and Brick Kilns," Mathematics, MDPI, vol. 9(17), pages 1-15, August.
    6. Hui Yu & Wei Wang & Baohua Yang & Cunfang Li, 2019. "Evolutionary Game Analysis of the Stress Effect of Cross-Regional Transfer of Resource-Exhausted Enterprises," Complexity, Hindawi, vol. 2019, pages 1-16, November.
    7. Méndez-Gordillo, Alma Rosa & Cadenas, Erasmo, 2021. "Wind speed forecasting by the extraction of the multifractal patterns of time series through the multiplicative cascade technique," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    8. Yuliang Yang & Chaoqun Cui, 2022. "Which Provincial Regions in China Should Give Priority to the Redevelopment of Abandoned Coal Mines? A Redevelopment Potential Evaluation Based Analysis," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
    9. Wang, Qiang & Song, Xiaoxin, 2021. "How UK farewell to coal – Insight from multi-regional input-output and logarithmic mean divisia index analysis," Energy, Elsevier, vol. 229(C).
    10. Shengli Liao & Xudong Tian & Benxi Liu & Tian Liu & Huaying Su & Binbin Zhou, 2022. "Short-Term Wind Power Prediction Based on LightGBM and Meteorological Reanalysis," Energies, MDPI, vol. 15(17), pages 1-21, August.
    11. Wasilewski, J. & Baczynski, D., 2017. "Short-term electric energy production forecasting at wind power plants in pareto-optimality context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 177-187.
    12. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    13. Kim, Deockho & Hur, Jin, 2018. "Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method," Energy, Elsevier, vol. 157(C), pages 211-226.
    14. Wang, Jujie & Li, Yaning, 2018. "Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy," Applied Energy, Elsevier, vol. 230(C), pages 429-443.
    15. Ruoxuan Xia & Zhuoming Long & Licong Xing & Yousaf Ali Khan, 2023. "Achieving sustainable development through economic growth, energy consumption, and agricultural productivity in China," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(5), pages 3428-3442, October.
    16. Zhao, Changhong & Zhang, Weirong & Wang, Yang & Liu, Qilin & Guo, Jingsheng & Xiong, Minpeng & Yuan, Jiahai, 2017. "The economics of coal power generation in China," Energy Policy, Elsevier, vol. 105(C), pages 1-9.
    17. Xiang Ying & Keke Zhao & Zhiqiang Liu & Jie Gao & Dongxiao He & Xuewei Li & Wei Xiong, 2022. "Wind Speed Prediction via Collaborative Filtering on Virtual Edge Expanding Graphs," Mathematics, MDPI, vol. 10(11), pages 1-16, June.
    18. Zhang, Lixiao & Yang, Min & Zhang, Pengpeng & Hao, Yan & Lu, Zhongming & Shi, Zhimin, 2021. "De-coal process in urban China: What can we learn from Beijing's experience?," Energy, Elsevier, vol. 230(C).
    19. Costa, Marcelo Azevedo & Ruiz-Cárdenas, Ramiro & Mineti, Leandro Brioschi & Prates, Marcos Oliveira, 2021. "Dynamic time scan forecasting for multi-step wind speed prediction," Renewable Energy, Elsevier, vol. 177(C), pages 584-595.
    20. Lu, Zhijian & Shao, Shuai, 2016. "Impacts of government subsidies on pricing and performance level choice in Energy Performance Contracting: A two-step optimal decision model," Applied Energy, Elsevier, vol. 184(C), pages 1176-1183.

    More about this item

    Statistics

    Access and download statistics

    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:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-04505-8. 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: https://www.nature.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.