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

Guoquan Zhang, Guohao Li and Jing Peng
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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

Sustainability, 2020, vol. 12, issue 18, 1-20

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.

Keywords: risk assessment; fresh produce; green logistics; support vector machine (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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