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Risk Assessment and Monitoring of Green Logistics for Fresh Produce Based on a Support Vector Machine

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  • Guoquan Zhang

    (School of Management, Jilin University, Changchun 130022, China)

  • Guohao Li

    (School of Management, Jilin University, Changchun 130022, China)

  • Jing Peng

    (College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China)

Abstract

The sustainability and profitability of fresh produce supply chains are contingent upon several risk factors. This work, therefore, examines several risk indicators that affect the quality and safety of fresh produce in transit, including technological, biological, sustainability, environmental, and emergency risks. Then, we developed a risk assessment and monitoring model that employs a machine learning algorithm, a support vector machine, based on historical monitoring data. The proposed methodology was then applied to simulation and numerical analysis to assess the risks incurred in the strawberry cold chain. After training, the algorithm predicted the risks incurred during transportation with an average accuracy of 90.4%. Therefore, the developed methodology can effectively and accurately perform a risk assessment. Furthermore, the risk assessment model can be applied to other fresh produce due to comprehensive risk indicators. Decision-makers in fresh produce logistics companies can use the developed methodology to identify and mitigate risks incurred, thus improving food safety, reducing product loss, maximizing profits, and realizing sustainable development.

Suggested Citation

  • Guoquan Zhang & Guohao Li & Jing Peng, 2020. "Risk Assessment and Monitoring of Green Logistics for Fresh Produce Based on a Support Vector Machine," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:18:p:7569-:d:413533
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    References listed on IDEAS

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    Cited by:

    1. Juntao Li & Tianxu Cui & Kaiwen Yang & Ruiping Yuan & Liyan He & Mengtao Li, 2021. "Demand Forecasting of E-Commerce Enterprises Based on Horizontal Federated Learning from the Perspective of Sustainable Development," Sustainability, MDPI, vol. 13(23), pages 1-29, November.
    2. Magdalena Scherer & Piotr Milczarski, 2021. "Machine-Learning-Based Carbon Footprint Management in the Frozen Vegetable Processing Industry," Energies, MDPI, vol. 14(22), pages 1-21, November.
    3. WANG, Yang & Liu, Dinghan & Sui, Xiuping & Li, Fengchun, 2022. "Does logistics efficiency matter? Evidence from green economic efficiency side," Research in International Business and Finance, Elsevier, vol. 61(C).

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