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Mathematics
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Mathematical Modeling, Intelligent Manufacturing and Intelligent Production Systems
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Mathematical Modeling, Intelligent Manufacturing and Intelligent Production Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 3902

Special Issue Editors

Prof. Dr. Qingdang Li

E-Mail Website
Guest Editor
College of Sino-German Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China
Interests: intelligent manufacturing; intelligent robot; industry 4.0; intelligent systems; industrial internet; digital twin; microelectromechanical systems (MEMS)
Dr. Zhen Sun

E-Mail Website
Guest Editor
College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China
Interests: intelligent systems; artificial intelligence; computer vision; industrial data analysis and processing

Special Issue Information

Dear Colleagues,

Intelligent manufacturing is an important and developing direction in the industrial field. In the future, industrial production will shift from mass production to mass customized production, achieving lower production costs, higher production efficiency and more environmentally friendly production processes. In this development process, some technologies—industrial mathematical modeling, industrial simulation, mathematical methods, intelligent robots, industrial internet, cyber-physical systems, digital twin, 5G, Big Data, etc.—will become the main technical framework. Industrial mathematical modeling and control theory will provide control algorithms and optimization methods for the production process. Artificial intelligence, computer vision, and industrial data analysis will provide key basic technologies in the intelligent manufacturing system and promote the transformation of traditional production processes to intelligent manufacturing systems.

Therefore, this Special Issue will collect a series of the latest research articles on intelligent manufacturing and related mathematical modeling and simulation methods, including but not limited to, intelligent manufacturing systems, industrial simulation and mathematical modeling, industrial control and optimization, MEMS, computer vision, data analysis and other topics. Your contributions will promote the development of the field of intelligent manufacturing through high-quality theoretical research, such as mathematical methods and experimental research by applied mathematics. The selection criteria consider the formal and technical soundness, experimental support, and relevance of contributions.

Prof. Dr. Qingdang Li
Dr. Zhen Sun
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent manufacturing
  • intelligent system
  • mathematical modeling methods
  • applied mathematics in the industry
  • control theory
  • intelligent robot
  • industrial internet
  • mathematical methods
  • artificial intelligence
  • data analysis

Published Papers (2 papers)

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Research

19 pages, 2573 KiB  
Article
Demographic Factors Affecting Fuzzy Grading: A Hierarchical Linear Regression Analysis
by Daniel Doz, Darjo Felda and Mara Cotič
Mathematics 2023, 11(6), 1488; https://doi.org/10.3390/math11061488 - 18 Mar 2023
Cited by 2 | Viewed by 1685
Abstract
Several factors affect students’ mathematics grades and standardized test results. These include the gender of the students, their socio-economic status, the type of school they attend, and their geographic region. In this work, we analyze which of these factors affect assessments of students [...] Read more.
Several factors affect students’ mathematics grades and standardized test results. These include the gender of the students, their socio-economic status, the type of school they attend, and their geographic region. In this work, we analyze which of these factors affect assessments of students based on fuzzy logic, using a sample of 29,371 Italian high school students from the 2018/19 academic year. To combine grades assigned by teachers and the students’ results in the INVALSI standardized tests, a hybrid grade was created using fuzzy logic, since it is the most suitable method for analyzing qualitative data, such as teacher-given grades. These grades are analyzed with a hierarchical linear regression. The results show that (1) boys have higher hybrid grades than girls; (2) students with higher socio-economic status achieve higher grades; (3) students from scientific lyceums have the highest grades, whereas students from vocational schools have the lowest; and (4) students from Northern Italy have higher grades than students from Southern Italy. The findings suggest that legislators should investigate appropriate ways to reach equity in assessment and sustainable learning. Without proper interventions, disparities between students might lead to unfairness in students’ future career and study opportunities. Full article
(This article belongs to the Special Issue Mathematical Modeling, Intelligent Manufacturing and Intelligent Production Systems)
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17 pages, 4559 KiB  
Article
Gathering Strength, Gathering Storms: Knowledge Transfer via Selection for VRPTW
by Wendi Xu, Xianpeng Wang, Qingxin Guo, Xiangman Song, Ren Zhao, Guodong Zhao, Yang Yang, Te Xu and Dakuo He
Mathematics 2022, 10(16), 2888; https://doi.org/10.3390/math10162888 - 12 Aug 2022
Cited by 8 | Viewed by 1636
Abstract
Recently, due to the growth in machine learning and data mining, for scheduling applications in China’s industrial intelligence, we are quite fortunate to witness a paradigm of evolutionary scheduling via learning, which includes a new tool of evolutionary transfer optimization (ETO). As a [...] Read more.
Recently, due to the growth in machine learning and data mining, for scheduling applications in China’s industrial intelligence, we are quite fortunate to witness a paradigm of evolutionary scheduling via learning, which includes a new tool of evolutionary transfer optimization (ETO). As a new subset in ETO, single-objective to multi-objective/many-objective optimization (SMO) acts as a powerful, abstract and general framework with wide industrial applications like shop scheduling and vehicle routing. In this paper, we focus on the general mechanism of selection that selects or gathers elite and high potential solutions towards gathering/transferring strength from single-objective problems, or gathering/transferring storms of knowledge from solved tasks. Extensive studies in vehicle routing problems with time windows (VRPTW) on well-studied benchmarks validate the great universality of the SMO framework. Our investigations (1) contribute to a deep understanding of SMO, (2) enrich the classical and fundamental theory of building blocks for genetic algorithms and memetic algorithms, and (3) provide a completive and potential solution for VRPTW. Full article
(This article belongs to the Special Issue Mathematical Modeling, Intelligent Manufacturing and Intelligent Production Systems)
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