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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: closed (31 August 2024) | Viewed by 8666

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

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Published Papers (9 papers)

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Research

27 pages, 1882 KiB  
Article
A Fuzzy Method for Exploring Key Factors of Smart Healthcare to Long-Term Care Based on Z-Numbers
by Chen-Tung Chen and Chien-Chi Chu
Mathematics 2024, 12(22), 3471; https://doi.org/10.3390/math12223471 - 6 Nov 2024
Viewed by 503
Abstract
As the proportion of the population comprising the elderly cohort increases, so too does the demand for medical care for long-term conditions among this demographic. The advent of information technology and artificial intelligence has prompted a crucial examination of the potential of smart [...] Read more.
As the proportion of the population comprising the elderly cohort increases, so too does the demand for medical care for long-term conditions among this demographic. The advent of information technology and artificial intelligence has prompted a crucial examination of the potential of smart medical technology and equipment to enhance the quality of long-term care and the operational efficiency of long-term care facilities. The introduction of smart healthcare into long-term care is influenced by a few factors, and expert opinions often exhibit ambiguity and subjectivity in the evaluation process. As Z-numbers are capable of adequately expressing the ambiguity of expert assessments and the degree of certainty associated with them, they are employed in this study to convey the opinions of the experts. Furthermore, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is an effective approach to analyzing the relationships between factors. Consequently, this study integrates the Z-numbers and DEMATEL methods for empirical analysis. The present study focuses on two long-term care institutions with different natures as empirical subjects. The findings of the study indicate that Institution A identifies the “Internet of Things” as the most pivotal key factor, whereas Institution B deems “Smart clinics and urgent care centers” to be the most crucial key factor. The analysis demonstrates that three factors—global positioning systems, telemedicine, and electronic medical records—are all regarded as significant influencing factors for different long-term care institutions. Consequently, the analytical model of this study is not only theoretically sound but also effective in identifying the key factors and importance of introducing smart healthcare into long-term care institutions. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering, 3rd Edition)
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26 pages, 10523 KiB  
Article
Fuzzy Logic Controller for Power Control of an Electric Arc Furnace
by Loredana Ghiormez, Manuela Panoiu and Caius Panoiu
Mathematics 2024, 12(21), 3445; https://doi.org/10.3390/math12213445 - 4 Nov 2024
Viewed by 625
Abstract
Electric Arc Furnaces (EAFs) are widely used in the steel manufacturing industry to melt scrap steel by employing a large number of electric arcs. EAFs play an important role in ensuring the efficient production of steel. However, their nonlinear and variable load characteristics [...] Read more.
Electric Arc Furnaces (EAFs) are widely used in the steel manufacturing industry to melt scrap steel by employing a large number of electric arcs. EAFs play an important role in ensuring the efficient production of steel. However, their nonlinear and variable load characteristics have a significant impact on power quality. Because the active power of an electric arc depends on its length, a system for controlling the electrode positions is necessary. This paper presents a control system based on a fuzzy logic controller for the active power control of an electric arc furnace. Individual simulation scenarios were chosen with both reference values and the process taken into consideration. The reference, constant value, step variation, and the sequence of step variation were investigated, as well as step disturbances and the sequence of step disturbances from the viewpoint of the process. Furthermore, the procedure of changing the tap on a transformer was investigated. The proposed solution minimizes the time required for charge elaboration, but the main benefit is that there are no additional costs in the implementation process because the installation remains identical, with the only changes being improvements to soft control management. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering, 3rd Edition)
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27 pages, 5811 KiB  
Article
Advanced Study: Improving the Quality of Cooling Water Towers’ Conductivity Using a Fuzzy PID Control Model
by You-Shyang Chen, Ying-Hsun Hung, Mike Yau-Jung Lee, Jieh-Ren Chang, Chien-Ku Lin and Tai-Wen Wang
Mathematics 2024, 12(20), 3296; https://doi.org/10.3390/math12203296 - 21 Oct 2024
Viewed by 673
Abstract
Cooling water towers are commonly used in industrial and commercial applications. Industrial sites frequently have harsh environments, with certain characteristics such as poor air quality, close proximity to the ocean, large quantities of dust, or water supplies with a high mineral content. In [...] Read more.
Cooling water towers are commonly used in industrial and commercial applications. Industrial sites frequently have harsh environments, with certain characteristics such as poor air quality, close proximity to the ocean, large quantities of dust, or water supplies with a high mineral content. In such environments, the quality of electrical conductivity in the cooling water towers can be significantly negatively affected. Once minerals (e.g., calcium and magnesium) form in the water, conductivity becomes too high, and cooling water towers can become easily clogged in a short time; this leads to a situation in which the cooling water host cannot be cooled, causing it to crash. This is a serious situation because manufacturing processes are then completely shut down, and production yield is thus severely reduced. To solve these problems, in this study, we develop a practical designation for a photovoltaic industry company called Company-L. Three control methods are proposed: the motor control method, the PID control method, and the fuzzy PID control method. These approaches are proposed as solutions for successfully controlling the forced replenishment and drainage of cooling water towers and controlling the opening of proportional control valves for water release; this will further dilute the electrical conductivity and control it, bringing it to 300 µS/cm. In the experimental processes, we first used practical data from Company-L for our case study. Second, from the experimental results of the proposed model for the motor control method, we can see that if electrical conductivity is out of control and the conductivity value exceeds 1000 µS/cm, the communication software LINE v8.5.0 (accessible via smartphone) displays a notification that the water quality of the cooling water towers requires attention. Third, although the PID control method is shown to have errors within an acceptable range, the proportional (P) controller must be precisely controlled; this control method has not yet reached this precise control in the present study. Finally, the fuzzy PID control method was found to have the greatest effect, with the lowest level of errors and the most accurate control. In conclusion, the present study proposes solutions to reduce the risk of ice-water host machines crashing; the solutions use fuzzy logic and can be used to ensure the smooth operation of manufacturing processes in industries. Practically, this study contributes an applicable technical innovation: the use of the fuzzy PID control model to control cooling water towers in industrial applications. Concurrently, we present a three-tier monitoring checkpoint that contributes to the PID control method. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering, 3rd Edition)
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20 pages, 6360 KiB  
Article
Design of a Robust Controller for Induction Motor Drive Systems Based on Extendable Fuzzy Theory
by Kuei-Hsiang Chao and Cheng-Lung Chang
Mathematics 2024, 12(20), 3235; https://doi.org/10.3390/math12203235 - 16 Oct 2024
Viewed by 551
Abstract
In this paper, an extendable fuzzy robust speed controller suitable for induction motor drive systems was proposed. Firstly, the two-degrees-of-freedom (2DOF) robust control technology with feedforward control and disturbance elimination method was adopted. Upon parameter variation and load disturbance, the motor drive system [...] Read more.
In this paper, an extendable fuzzy robust speed controller suitable for induction motor drive systems was proposed. Firstly, the two-degrees-of-freedom (2DOF) robust control technology with feedforward control and disturbance elimination method was adopted. Upon parameter variation and load disturbance, the motor drive system could utilize a robust controller to generate compensation signals and reduce the impact on the controlling performance of the motor drive system. The magnitude of the compensation signal was adjusted via the weighting factor. However, should a fixed weighting factor be adopted, system instability might be generated easily when time delay and saturation of control force occur. Based on the above, the smart method of extendable fuzzy theory (EFT) was adopted in this paper to adjust adequate weighting factors, where the controlling performance of the induction motor drive system could be improved accordingly. Lastly, the simulation software Matlab/Simulink (R2023b version) was applied to simulate the utilization of the controlling method proposed for the induction motor drive system. The simulation results proved that the extendable fuzzy robust speed controller proposed provided better speed tracking and load regulation-controlling performance than the conventional robust controller. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering, 3rd Edition)
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15 pages, 308 KiB  
Article
Evaluating Order Allocation Sustainability Using a Novel Framework Involving Z-Number
by Kuan-Yu Lin, Cheng-Lu Yeng and Yi-Kuei Lin
Mathematics 2024, 12(16), 2585; https://doi.org/10.3390/math12162585 - 21 Aug 2024
Viewed by 579
Abstract
The United Nations’ sustainable development goals have highlighted the significance of improving supply chain sustainability and ensuring the proper distribution of orders. This study proposes a novel framework involving Z-number, game theory, an indifference threshold-based attribute ratio analysis (ITARA), and a combined compromise [...] Read more.
The United Nations’ sustainable development goals have highlighted the significance of improving supply chain sustainability and ensuring the proper distribution of orders. This study proposes a novel framework involving Z-number, game theory, an indifference threshold-based attribute ratio analysis (ITARA), and a combined compromise solution method (CoCoSo) to evaluate the sustainability of suppliers and order allocations. To better reflect the decision makers’ current choices for the sustainability of assessed suppliers and order allocations and enhance the comprehensiveness of decision-making, the importance parameter of the supplier is obtained through game theory objectively for transforming supplier performance into order allocation performance. The Z-numbers are involved in ITARA (so-called ZITARA) and CoCoSo (so-called ZCoCoSo) to overcome the issue of information uncertainty in the process of expert evaluation. ZITARA and ZCoCoSo are used to determine the objective weights of criteria and to rank the evaluated order allocations, respectively. A case study of a China company is then presented to demonstrate the usefulness of the proposed framework and to inform their decision-making process regarding which suppliers the orders should be assigned to. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering, 3rd Edition)
25 pages, 4042 KiB  
Article
A Model for Developing a Mobile Payment Service Framework
by Amy H. I. Lee and He-Yau Kang
Mathematics 2024, 12(13), 2052; https://doi.org/10.3390/math12132052 - 30 Jun 2024
Viewed by 1187
Abstract
The rise of wireless communication has spurred the global adoption of mobile payment services, a trend that is significantly reducing the use of cash. This shift, driven by new technologies and lifestyle changes, not only presents opportunities for businesses but also enhances consumers’ [...] Read more.
The rise of wireless communication has spurred the global adoption of mobile payment services, a trend that is significantly reducing the use of cash. This shift, driven by new technologies and lifestyle changes, not only presents opportunities for businesses but also enhances consumers’ daily activities. Consumers’ and businesses’ willingness to adopt mobile payment services has increased due to factors such as easier access to new technologies, convenience, changing lifestyle choices, and economic conditions. Despite challenges such as limited access to technology, security concerns, and high transaction fees, the potential benefits of mobile payment services are promising. Therefore, this research aims to construct a suitable model for developing a mobile payment service framework that both consumers and businesses are willing to adopt. The proposed model integrates the Delphi method, interpretive structural modeling (ISM), quality function deployment (QFD), an analytic network process (ANP), and fuzzy set theory. To demonstrate the practical application of the model, a case study of developing a mobile payment service framework is presented, showcasing how the model can be used to address real-world challenges and enhance the adoption of mobile payment services. The case study results show that ease of use, system and service quality, and reliability are the most important customer requirements, and encryption, edge computing, authentication, and interoperability are the most important engineering characteristics. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering, 3rd Edition)
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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 2419
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, 356 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 649
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)
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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 718
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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