Abstract
At present, the “first-mile” of fruit and vegetable products in China has problems such as low efficiency of manual grading and untimely pre-cooling services, and the scattered small and medium scale characteristics of fruit and vegetable producing areas make it difficult to implement post-harvest processes. The emerging mobile grading and pre-cooling equipment used in the field can solve these problems, but there is a break in the chain between grading and pre-cooling, and mobile grading and pre-cooling resources need to be coordinated to reduce the service interval and thus reduce the rate of fruit and vegetable spoilage. In this paper, taking into account the service sequencing constraints of graded pre-cooling, the maximum service interval constraint of graded pre-cooling and the delayed pre-cooling effect, and other factors, a two-demand collaborative optimization model for mobile graded pre-cooling is developed to minimize the total cost. A hybrid genetic algorithm with variable neighborhood search considering customer clustering is developed to solve the problem and compared with genetic algorithms and variable neighborhood search algorithms to verify its effectiveness. The rationality of the model is proved by solving a case of grading and pre-cooling apples in Luochuan County, Shaanxi Province, and designing a sensitivity analysis to find the most suitable service level and percentage of users with pre-cooling needs for the county, which provides a basis for decision making for the future use of mobile grading and pre-cooling resources in the promotion of small and medium-sized farmers.
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Acknowledgements
This work is supported in part by the National Key Research and Development Program of China under Grant 2019YFD1101103, the National Natural Science Foundation of China under Grants 72071028, 71973106, and 71933005, and Annual Project of Social Science Foundation of Shaanxi Province under Grant 2020R002.
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Wang, X., Wang, Y., lin, N., Li, Y. (2022). Synchronization of Mobile Grading and Pre-cooling Services. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 309. Springer, Singapore. https://doi.org/10.1007/978-981-19-3444-5_27
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DOI: https://doi.org/10.1007/978-981-19-3444-5_27
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