After completing the market positioning of products, catering for the functional and emotional needs of consumers is the key to achieve the success of product design. In the present study, the psychological theory is employed to screen the subjects and quantitatively evaluate the alternative design schemes to ensure the optimal design scheme. Then, an evaluation method of product appearance design based on eye tracking and aesthetic perception measurement is obtained. With the design of campus street lamp as an example, this paper evaluates the appearance design of products using emotional measurement and eye tracking technology. IAPS is used to screen out the participants who can represent the emotions and aesthetic experience of the public. Subsequently, the index system of aesthetic evaluation is established according to aesthetic theory, and the transformation relationship between aesthetic evaluation index and physiological eye movement index is determined. Finally, the eye tracking technology is used to evaluate the aesthetic feeling of each alternative, and the best design schemes are obtained. This study aims to explore a simple, practical, and low-cost product design evaluation method. As a hotspot in the research field of product design methodology, emotional design has broad prospects for application. Moreover, this paper also proposes a new direction for the development of emotional design research, which is based on quantitative analysis with the supplement of qualitative analysis.

In the job-shop scheduling field, timely and proper updating of the original scheduling strategy is an effective way to avoid the negative impact of disturbances on manufacturing. In this paper, a pure reactive scheduling method for updating the scheduling strategy is proposed to deal with the disturbance of the uncertainty of the arrival of new jobs in the job shop. The implementation process is as follows: combine data mining, discrete event simulation, and dispatching rules (DRs), take makespan and machine utilization as scheduling criteria, divide the manufacturing system production period into multiple scheduling subperiods, and build a dynamic scheduling model that assigns DRs to subscheduling periods in real-time; the scheduling strategies are generated at the beginning of each scheduling subperiod. The experiments showed that the method proposed enables a reduction in the makespan of 2–17% and an improvement in the machine utilization of 2–21%. The constructed scheduling model can assign the optimal DR to each scheduling subperiod in real-time, which realizes the purpose of locally updating the scheduling strategy and enhancing the overall scheduling effect of the manufacturing system.