Modern Management Science & Engineering
type: channel
description: Modern Management Science & Engineering (MMSE) is an international, Refereed, double-blind peer-reviewed, open-access journal published by Scholink. The mission of MMSE is to provide a high-level platform for experts and scholars all over the world to share their latest ideas of management and foster the prosperity of the discipline. We would welcome scholars and researchers engaging in the related field to submit your manuscripts to MMSE. Both of online submission and E-mail submission (mmse@scholink.org) are acceptable. We would welcome scholars and researchers engaging in the related field but is not limited to the following fields: Public Management and Engineering Management Science Theories and Methods Business Management Engineering Management Loop Management Agricultural Engineering Management Industrial Engineering and Operations Management: Policy and Risk Management Operations and Supply Chain Management Sustainable Management and Green Engineering Strategic Management and Organizational Behavior Integration of Information Technology and Management Others Both of online submission and E-mail submission (mmse@scholink.org) are acceptable. ------------------------------------------------------------------------ Open access: Modern Management Science & Engineering is available online to the reader "without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself." Peer review: Modern Management Science & Engineering takes peer review policy. Peer review is the evaluation of work by one or more people of similar competence to the producers of the work (peers). <img src="[binary]" alt="" />
publisher: SCHOLINK INC.
language: en-US
publication name: Modern Management Science & Engineering
issn: 2052-2576
copyright: Authors who publish with this journal must agree to the following terms: Authors should promise that their works are entirely original, and all the references in their works should be appropriately cited or quoted. The works have not been published previously. The behavior of submitting the same work to other journals is unacceptable. The journal editor has the right to edit the format of works.
items: Research on Production Line Balance Optimization Based on Improved Genetic Algorithm
items: Research on the Influence of Entrepreneurship on New Quality Productivity
items: The Application of Artificial Intelligence and Big Data in Financial Risk Forecasting and Management

Research on Production Line Balance Optimization Based on Improved Genetic Algorithm
type: item
description: With the rapid development of China's manufacturing industry and information technology, higher requirements are put forward for the efficiency of electronic product production lines such as mobile phones. XY Company is a manufacturing enterprise mainly producing mobile phones and other electronic products, but its workshop production line has the phenomenon of unbalanced operation load of each station and low production line balance rate, this paper uses industrial engineering methods and dual-population genetic algorithm to optimize its production line, the results show that the optimized production line balance rate increases from 55.89% to 97.97%, the production line smoothing index decreases from 294.37 to 4.82, and the production line balance state is significantly improved.
creator: Shanping Xu
creator: Tao Li
rights: Copyright (c) 2025 Shanping Xu, Tao Li http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2025-11-18
publication date: 2025-11-18
volume: 7
number: 1
starting page: 1
doi: 10.22158/mmse.v7n1p1

Research on the Influence of Entrepreneurship on New Quality Productivity
type: item
description: the new development of productivity is our country to construct a new competitive advantage and win the strategic choice for the development of the initiative, entrepreneurship penetration into the production of each link, the new catalytic productivity. Articles intended combing new concept and connotation of productive forces, the entrepreneurial spirit to promote new mediation effect of productivity, and corporate characteristics in entrepreneurship for new show heterogeneity in the influence of the mass productivity. The results show that entrepreneurship has a significant effect on the promotion of new quality productivity; The mechanism test shows that entrepreneurship drives the new quality productivity by improving the quality of knowledge spillover, alleviating financial constraints, and technological innovation and application. The results of heterogeneity analysis show that entrepreneurship plays a more prominent role in promoting the new quality productivity in the enterprises with higher executive salary and private property. This study adds new theory of productivity blueprint, for entrepreneurship, better fitting new mass productivity development direction to provide theoretical explanation and policy recommendations.
creator: Xinlin Liao
creator: Yiqin Lin
rights: Copyright (c) 2025 Xinlin Liao, Yiqin Lin http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2025-12-03
publication date: 2025-12-03
volume: 7
number: 1
starting page: 15
doi: 10.22158/mmse.v7n1p15

The Application of Artificial Intelligence and Big Data in Financial Risk Forecasting and Management
type: item
description: In the past, financial risk management has always faced a changing market environment and while there were several traditional predictive models available for use, they have limited capabilities in addressing non-linear or high-dimensional risk factors. Therefore, enhancing the foresight in risk identification as well as the accuracy of decision-making within financial institutions will require further exploration into the application value of artificial intelligence and big data technology. Consequently, the goal of this paper is to conduct a comprehensive evaluation regarding the integration pathways and effectiveness of implementing these two technologies into financial risk predicting and managing practices. Findings from the research indicate that AI algorithms offer a deeper understanding of complex, hidden relationships among large amounts of multiple sources of information, while the availability of big data platforms provides opportunities for continuously monitoring risk in real-time. The combination of the two not only significantly improves the timeliness and accuracy of risk prediction, but also promotes the intelligent transformation of risk management models from passive response to active intervention.
creator: Shenglin Yang
rights: Copyright (c) 2025 Shenglin Yang http://creativecommons.org/licenses/by/4.0
license: http://creativecommons.org/licenses/by/4.0
date: 2025-12-09
publication date: 2025-12-09
volume: 7
number: 1
starting page: 34
doi: 10.22158/mmse.v7n1p34