Path optimization of cold chain logistics (CCL) is an important subject for scientists to explore. This paper mainly studies the optimization algorithm of CCL distribution vehicle scheduling based on cloud computing. By analyzing the characteristics of CCL, an optimization model of the delivery vehicle path is established, including the service time within the optimal time range required by the customer and the delivery vehicle arriving in advance. Next, the classification of multi-source input data for vehicle dynamic route optimization modeling is analyzed, and the cloud computing resource integration technology is used to comprehensively process the multi-source data. Experimental data shows that, taking into account the divisible demand, the total circulation fee is 7356.92 yuan; the fixed fee is 2717 yuan; the transportation fee is 3245 yuan. 19 refrigerated trucks are used, and the vehicle loading rate is 95.2%. The findings indicate that employing cloud computing techniques to enhance the routing of cold chain logistics vehicles is efficacious and possesses notable theoretical implications as well as practical utility.
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