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.
In the digital era, data mining and statistical analysis have been widely used to solve problems, especially in the field of management and engineering. Therefore, we aim to make a new insight of human resource management based on multiple regression modelling and quantile regression modelling. Specifically, the systematic framework of job satisfaction in this research is constructed by three dimensions from the perspective of psychology, namely, the perception of interpersonal relationship, financial compensation, and work conditions. Each dimension consists of two measures which reflect the employees’ view towards them. The empirical estimation results show the following. (1) Perceived relationship with managers, perceived rationality of compensation, perceived match degree of job, and perceived autonomy degree of work are all significantly positively correlated with job satisfaction. (2) The effect of perceived rationality of compensation is significantly different between the high quantile and the low quantile. For those with lower perceived rationality of compensation, their job satisfaction is more likely to be affected due to the perceived compensation than those with higher perception. This research enriches the existing theory by constructing a comprehensive framework of the influencing factors of job satisfaction, which provides useful implications of human resource management optimization for enterprises.
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