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Mathematics
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Fuzzy Applications in Industrial Engineering, 3rd Edition
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Fuzzy Applications in Industrial Engineering, 3rd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 1462

Special Issue Editors

Prof. Dr. Kuen-Suan Chen

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Guest Editor
Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan
Interests: statistical process control; fuzzy decision making; quality management; process capability analysis; six sigma; service management
Special Issues, Collections and Topics in MDPI journals
Dr. Chun-Min Yu

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan
Interests: statistical fuzzy methodology; statistical process control; process quality analysis; six sigma methodology and applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ying Yang

E-Mail Website
Guest Editor
Newcastle University Business School, Newcastle University, Newcastle upon Tyne NE1 4SE, UK
Interests: supply chain resilience and risk management; behavioural supply chain management and decision making; supply chain relationships, strategy, and sustainability; operations strategy (postponement, lean and decoupling points); service operations management; knowledge management in organisations

Special Issue Information

Dear Colleagues,

Industrial engineering (IE) involves the design, improvement, and installation of integrated systems of people, material, equipment, and energy. Industrial engineers are engaged in reducing production costs, increasing efficiency, and improving the quality of products and services. Fuzzy set approaches are usually the most appropriate when human evaluations and the modeling of human knowledge are needed. IE uses a significant number of applications of the fuzzy set theory.

The purpose of this Special Issue is to gather a collection of articles reflecting the latest developments in the different fields of industrial engineering that apply the fuzzy set theory and fuzzy logic for control and reliability, manufacturing systems and technology management, optimization techniques, quality management, process capability analysis, statistical decision-making, and others.

Prof. Dr. Kuen-Suan Chen
Dr. Chun-Min Yu
Prof. Dr. Ying Yang
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

  • fuzzy set theory
  • fuzzy logic
  • fuzzy applications
  • fuzzy control and reliability
  • fuzzy manufacturing systems
  • fuzzy optimization techniques
  • fuzzy service performance evaluation
  • fuzzy process capability analysis
  • fuzzy statistical decision-making
  • operators and fuzzy arithmetic

Published Papers (3 papers)

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Research

33 pages, 2403 KiB  
Article
The Development Trends of Computer Numerical Control (CNC) Machine Tool Technology
by Kai-Chao Yao, Dyi-Cheng Chen, Chih-Hsuan Pan and Cheng-Lung Lin
Mathematics 2024, 12(13), 1923; https://doi.org/10.3390/math12131923 - 21 Jun 2024
Viewed by 333
Abstract
In the industrial era, production equipment serves as an essential mother machine. In the global manufacturing industry, components such as laptop computers, mobile phones, and automotive parts all strive for aesthetic appearance. Taiwan’s machine tool industry plays a significant role globally. Faced with [...] Read more.
In the industrial era, production equipment serves as an essential mother machine. In the global manufacturing industry, components such as laptop computers, mobile phones, and automotive parts all strive for aesthetic appearance. Taiwan’s machine tool industry plays a significant role globally. Faced with the constantly changing market environment, the development and competitive advantage of CNC machines are crucial topics for manufacturers. Domestic manufacturers of computer numerical control machines should move towards the integration of automated equipment to accommodate various advanced parts processing procedures. Smart manufacturing will become the trend of the industry in the future. This study invited experts from academia, industry, and research institutions to conduct expert interviews. Their opinions were compiled and analyzed, supplemented by fuzzy Delphi analysis to establish the development trends of various modules. The feasibility and demand of the product’s functional technology for industrial development were analyzed under three research dimensions and eight technical items. A total of 26 key sub-technical items were identified, achieving an expert consensus level of over 80. Furthermore, the importance ranking was analyzed using the fuzzy analytic hierarchy process, and the consistency tests were passed with C.I. < 0.1 and C.R. < 0.1. Finally, the obtained importance ranking of the hierarchical structure was used to predict the future development of computer numerical control machines through a technology roadmap, helping manufacturers use it as a reference model for future development trends to enhance market competitiveness. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering, 3rd Edition)
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17 pages, 352 KiB  
Article
Bias-Correction Methods for the Unit Exponential Distribution and Applications
by Hua Xin, Yuhlong Lio, Ya-Yen Fan and Tzong-Ru Tsai
Mathematics 2024, 12(12), 1828; https://doi.org/10.3390/math12121828 - 12 Jun 2024
Viewed by 199
Abstract
The bias of the maximum likelihood estimator can cause a considerable estimation error if the sample size is small. To reduce the bias of the maximum likelihood estimator under the small sample situation, the maximum likelihood and parametric bootstrap bias-correction methods are proposed [...] Read more.
The bias of the maximum likelihood estimator can cause a considerable estimation error if the sample size is small. To reduce the bias of the maximum likelihood estimator under the small sample situation, the maximum likelihood and parametric bootstrap bias-correction methods are proposed in this study to obtain more reliable maximum likelihood estimators of the unit exponential distribution parameters. The procedure to implement the bias-corrected maximum likelihood estimation method is derived analytically, and the steps to obtain the bias-corrected bootstrap estimators are presented. The simulation results show that the proposed maximum likelihood bootstrap bias-correction method can significantly reduce the bias and mean squared error of the maximum likelihood estimators for most of the parameter combinations in the simulation study. A soil moisture data set and a numerical example are used for illustration. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering, 3rd Edition)
18 pages, 2010 KiB  
Article
Fuzzy Radar Evaluation Chart for Improving Machining Quality of Components
by Kuen-Suan Chen, Chun-Min Yu, Jin-Shyong Lin, Tsun-Hung Huang and Yun-Syuan Zhong
Mathematics 2024, 12(5), 732; https://doi.org/10.3390/math12050732 - 29 Feb 2024
Viewed by 499
Abstract
Some studies have shown that any part machined by an outsourcer usually has several basic quality characteristics. When the outsourcer’s process capabilities are insufficient, the defective rate of various quality characteristics of the product will increase, thereby raising the rework rate and scrap [...] Read more.
Some studies have shown that any part machined by an outsourcer usually has several basic quality characteristics. When the outsourcer’s process capabilities are insufficient, the defective rate of various quality characteristics of the product will increase, thereby raising the rework rate and scrap rate. As a result, maintenance costs will go up, economic value will decrease, and even carbon emissions can increase during the production process. In addition, the process capability index and the radar chart are widely used in engineering management and other fields. Since process indicators often contain unknown parameters, sample data are needed for evaluation. With the rapid development of the Internet of Things and big data analysis, many companies regard rapid response as a basic requirement for timeliness and cost consideration. Therefore, companies often have to evaluate the process quality of ten small samples and decide whether to make some improvements. In order to solve the above problems, this study proposed a fuzzy radar chart evaluation model for the process quality of multi-quality characteristic parts based on the process capability index. Using this model can help all parts manufacturers continue to improve the quality of their machined parts as well as reduce their rework and scrap rates. Meanwhile, carbon emissions can be lessened during the production process, and companies can fulfill their social responsibilities. This fuzzy radar chart evaluation model is based on confidence intervals. As the company’s past experience is incorporated, the evaluation accuracy can be maintained even with a smaller sample size. Furthermore, the fuzzy radar evaluation chart can simultaneously evaluate the process capabilities of all quality characteristics of the part. In addition to making it easier for manufacturers to master all quality characteristics, quality process capability can also help them seize improvement opportunities. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering, 3rd Edition)
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