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.

Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT) production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researchers still focus on small scale problems with one objective: a single machine environment. However, the scheduling problem is a multi-objective optimization problem in real applications. In this paper, a single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T), cost, and energy consumption simultaneously. An effective multi-objective evolutionary algorithm called local multi-objective evolutionary algorithm (LMOEA) is presented to tackle this multi-objective scheduling problem. To accommodate the characteristic of the problem, a new solution representation is proposed, which can convert discrete combinational problems into continuous problems. Additionally, a multiple local search strategy with self-adaptive mechanism is introduced into the proposed algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by instances with comparison to other multi-objective meta-heuristics such as Nondominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multiobjective Particle Swarm Optimization (OMOPSO), and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D). Experimental results demonstrate that the proposed LMOEA algorithm outperforms its counterparts for this kind of scheduling problems.

Currently, energy saving is increasingly important. During the production procedure, energy saving can be achieved if the operational method and machine infrastructure are improved, but it also increases the complexity of flow-shop scheduling. Actually, as one of the data mining technologies, Grey Wolf Optimization Algorithm is widely applied to various mathematical problems in engineering. However, due to the immaturity of this algorithm, it still has some defects. Therefore, we propose an improved multiobjective model based on Grey Wolf Optimization Algorithm related to Kalman filter and reinforcement learning operator, where Kalman filter is introduced to make the solution set closer to the Pareto optimal front end. By means of reinforcement learning operator, the convergence speed and solving ability of the algorithm can be improved. After testing six benchmark functions, the results show that it is better than that of the original algorithm and other comparison algorithms in terms of search accuracy and solution set diversity. The improved multiobjective model based on Grey Wolf Optimization Algorithm proposed in this paper is conducive to solving energy saving problems in flow-shop scheduling problem, and it is of great practical value in engineering and management.