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Modern information technologies such as big data and cloud computing are increasingly important and widely applied in engineering and management. In terms of cold chain logistics, data mining also exerts positive effects on it. Specifically, accurate prediction of cold chain logistics demand is conducive to optimizing management processes as well as improving management efficiency, which is the main purpose of this research. In this paper, we analyze the existing problems related to cold chain logistics in the context of Chinese market, especially the aspect of demand prediction. Then, we conduct the mathematical calculation based on the neural network algorithm and grey prediction. Two forecasting models are constructed with the data from 2013 to 2019 by R program 4.0.2, aiming to explore the cold chain logistics demand. According to the results estimated by the two models, we find that both of models show high accuracy. In particular, the prediction of neural network algorithm model is closer to the actual value with smaller errors. Therefore, it is better to consider the neural network algorithm as the first choice when constructing the mathematical forecasting model to predict the demand of cold chain logistic, which provides a more accurate reference for the strategic deployment of logistics management such as optimizing automation and innovation in cold chain processes to adapt to the trend.

Most traditional scheduling problems prioritize optimizing production efficiency, cost, and quality. However, with gradually increasing energy consumption and environmental pollution, the novel ''energy-efficient scheduling'' model has received increasing attention from scholars and engineers. This scheduling model focuses on reducing workshop energy consumption and environmental pollution and has become a hot topic in the scheduling area. This article proposes a new energy-efficient scheduling mathematical model considering productivity, energy efficiency, and noise reduction with flexible spindle speed for the job shop environment. This model considers the machining spindle speed that impacts the production time, power, and noise to be flexible and an independent decision-making variable. In addition, the evaluation methods of productivity, energy consumption, and noise are presented in this model. To cleanly solve this mixed integer programming model, an effective multi-objective genetic algorithm based on simplex lattice design is proposed. The corresponding encoding/decoding method, fitness function, and crossover/mutation operators are designed based on the features of this problem. To evaluate the performance of this method, three instances with different scales have been designed. The results demonstrate the effectiveness of the proposed model for the energy-efficient job shop scheduling problem.