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To respond to customer needs and achieve customized manufacturing, the manufacturing industry, as represented by electronics assembly companies, has embarked on a path of business model transformation (customer to manufacturer [C2M]). The purpose of this paper is to examine the practical application of assembly line-Seru conversion in a Chinese electronics assembly company during the C2M transition.

To begin with, this paper proposed a production line improvement scheme suitable for the conversion of C2M manufacturing enterprise assembly line-Seru based on an analysis of the difficulties encountered in the existing production line of A company in China. Then, a mathematical model was presented for the minimum value of the makespan and the maximum workers’ expenditure between Serus. Finally, the SA-NSGA-II algorithm and the entropy-weight TOPSIS approach were used to determine the optimal scheme for Seru unit, batch, product type and worker distribution.

Seru production and multiskilled workers are more suited to the C2M business model. The most effective strategy for worker allocation can reduce the number of employees and makespan in Serus. Additionally, the performance of the SA-NSGA-II algorithm and the method of selecting the optimal solution from the Pareto solution by the entropy-weighted TOPSIS method is also demonstrated.

Through a detailed study of how to transform the production line, other companies can apply the methods outlined in this article to shorten the delivery time, make full use of the abilities of workers and assign workers to specific positions, thereby reducing the number of workers, workers’ expenditure and improving the balance rate of production lines.

Given the scarcity of studies on the production method of C2M-type firms in the prior literature, this paper examined the assembly line-Seru conversion problem with the goal of minimizing the makespan and worker expenditure. To address the NSGA-II algorithm’s insufficient convergence, the simulated annealing process is incorporated into the method, which improves the optimization performance.

Online fresh food shopping has become increasingly popular in recent years, especially during the COVID-19 crisis. Online fresh food shopping provides consumers with an alternative to shopping in a traditional market, while also enabling procurement of such goods at a reduced risk of infection. The purpose of this study is to investigate whether online fresh food shopping behaviors change during public health emergency periods.

The data were collected through a web-based survey (508 respondents in China). Descriptive analysis, ordinal logistic regression analysis, and the Apriori algorithm were employed to explore what characteristics influence purchase frequency as well as food and delivery time preferences among different customer groups.

Based on the survey data, this study found that purchase frequency grew 71.2% during the COVID-19 crisis. City type and online shopping frequency of respondents are positively correlated with purchase frequency in normal and COVID-19 crisis periods. Number of daily hours worked by respondents only showed a significant impact for the normal period. People perceiving the risk of infection from going out are more willing to purchase fresh food online.

This is the first study to explore and compare online fresh food shopping behaviors during normal and COVID-19 crisis periods with a sample from China. The findings indicate a key role that online fresh food shopping can perform during a crisis and contribute to our understanding of fresh food online shopping behaviors during other possible public health emergency scenarios.

The new campus of Tianjin University was designed, built and now operates following a green and sustainable concept. The campus’ eco-friendly water environment was formed by establishing a water recycling system. The campus is divided into three drainage sections based on the masterplan. Each drainage section adopts different methods of collecting, utilizing and discharging water according to specific conditions, aimed at achieving both high drainage capability and the efficient utilisation of rainwater. The campus was designed so runoff pollution is reduced through the utilisation of low-impact development methods, ensuring the quality of the recharge water. Through studying the fundamentals of treatment measures and models for simulating water quality, water circulation, constructed wetlands and pollution control of rain runoff, parameters for efficient water recycling could be mathematically forecast, ensuring that stakeholders can be continuously engaged in improving and preserving the water quality of landscaped water on campus. The overall system integrates a variety of measures being implemented into one cohesive entity, which contributes to establishing the sustainable and healthy water cycling system of the green campus.