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Fuzzy Applications in Industrial Engineering
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Fuzzy Applications in Industrial Engineering

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 December 2021) | Viewed by 65803

Special Issue Editors

Prof. Dr. Kuen-Suan Chen

E-Mail Website
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. Tsang-Chuan Chang

E-Mail Website
Guest Editor
College of Intelligence, National Taichung University of Science and Technology, Taichung, Taiwan
Interests: statistical fuzzy methodology; statistical process control; process quality analysis; six sigma methodology and applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues

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

The purpose of this Special Issue is to gather a collection of articles reflecting the latest developments in different fields of industrial engineering by applying fuzzy theory 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
Prof. Dr. Chun-Min Yu
Prof. Dr. Tsang-Chuan Chang
Guest Editors

Manuscript Submission Information

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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.

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Keywords

  • Fuzzy Applications
  • Fuzzy Control and Reliability
  • Fuzzy Manufacturing Systems
  • Fuzzy Optimization Techniques
  • Fuzzy Service Performance Evaluation
  • Fuzzy Process Capability Analysis
  • Fuzzy Statistical Decision-making

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Published Papers (23 papers)

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Research

10 pages, 563 KiB  
Article
A Fuzzy Improvement Testing Model of Bank APP Performance
by Tian Chen, Ting-Hsin Hsu, Kuen-Suan Chen and Chun-Ming Yang
Mathematics 2022, 10(9), 1409; https://doi.org/10.3390/math10091409 - 22 Apr 2022
Viewed by 1530
Abstract
Numerous studies have pointed out that the issue of global warming is getting increasingly more serious. Therefore, the concepts of circular economy (CE) and sharing economy have been more and more valued by enterprises and governments. With the gradual popularization and maturity of [...] Read more.
Numerous studies have pointed out that the issue of global warming is getting increasingly more serious. Therefore, the concepts of circular economy (CE) and sharing economy have been more and more valued by enterprises and governments. With the gradual popularization and maturity of the Internet of Things (IoT), various smart APP platforms have sprung up rapidly. For example, the fuzzy evaluation model of bank APP performance was proposed in such an environment, aiming to improve the APP service performance by means of evaluating, analyzing, improving, and enhancing customers’ satisfaction with their use of APPs, and increasing the number of users of APPs. Since the follow-up of the article did not mention the improved testing model used to verify the improvement effect, this paper then proposed a fuzzy two-tailed testing model with two indices before and after the improvement based on the confidence interval to verify whether the improvement has had a significant effect. This complete bank APP fuzzy performance evaluation, analysis, and improvement model measured the bank APP operation performance using customer time intervals, so the data collection time was short. Not only can it meet enterprises’ need for rapid response and grasp the opportunity for improvement to achieve the effect of energy-saving and carbon reduction, but it also can satisfy enterprises’ requirement to pursue fast and accurate decision-making. Furthermore, the fuzzy two-tailed test proposed by this paper was based on the confidence interval, which can reduce the risk of misjudgment caused by sampling error. Plenty of studies have indicated that the designs based on confidence intervals can integrate expert experience and past data so that the accuracy of testing can be maintained in the case of small-sized samples. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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10 pages, 1798 KiB  
Article
Confidence-Interval-Based Fuzzy Testing for the Lifetime Performance Index of Electronic Product
by Chun-Min Yu, Kuen-Suan Chen and Ting-Hsin Hsu
Mathematics 2022, 10(9), 1405; https://doi.org/10.3390/math10091405 - 22 Apr 2022
Cited by 6 | Viewed by 1572
Abstract
When the lifetime of an electronic component does not reach the required level, it can be enhanced by means of the paralleling current sharing backup system or the redundant backup system. The lifetime of the redundant backup system is the sum of lifetimes [...] Read more.
When the lifetime of an electronic component does not reach the required level, it can be enhanced by means of the paralleling current sharing backup system or the redundant backup system. The lifetime of the redundant backup system is the sum of lifetimes of all electronic components, which is the maximum of all the electronic components’ lifetimes, compared with the lifetime of the parallel current sharing backup system. For the purpose of enhancing products’ reliability, electronic goods are usually designed with spare electronic components. If it is assumed that there are m−1 redundant backup components for each electronic product, then the lifetime of the electronic product will be distributed as a Gamma distribution with two parameters—m and λ, where λ is the mean for each lifetime of each electronic component. According to numerous studies, the sample size is not large, as it takes a long time to test the lifetime of an electronic product, and enterprises consider cost and timeliness. This paper concerns the performance index of the lifetime of the electronic product. Therefore, based on the confidence interval, this paper aims to develop a fuzzy testing model. As this model can integrate past data and expert experience, the testing accuracy can be retained despite small-sized samples. In fact, through adopting the testing model proposed by this paper, companies can make precise and intelligent decisions instantly with the use of small-sized samples to grasp the opportunities for improvement. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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30 pages, 4832 KiB  
Article
A Decision-Making Methodology Based on Expert Systems Applied to Machining Tools Condition Monitoring
by Manuel Casal-Guisande, Alberto Comesaña-Campos, Alejandro Pereira, José-Benito Bouza-Rodríguez and Jorge Cerqueiro-Pequeño
Mathematics 2022, 10(3), 520; https://doi.org/10.3390/math10030520 - 6 Feb 2022
Cited by 14 | Viewed by 3006
Abstract
The workers operating and supervising machining tools are often in charge of monitoring a high number of parameters of the machining process, and they usually make use of, among others, cutting sound signals, for following-up and assessing that process. The interpretation of those [...] Read more.
The workers operating and supervising machining tools are often in charge of monitoring a high number of parameters of the machining process, and they usually make use of, among others, cutting sound signals, for following-up and assessing that process. The interpretation of those signals is closely related to the operational conditions of the machine and to the work environment itself, because such signals are sensitive to changes in the process’ input parameters. Additionally, they could be considered as a valid indicator for detecting working conditions that either negatively affect the tools’ lifespan, or might even put the machine operators themselves at risk. In light of those circumstances, this work deals with the proposal and conceptual development of a new methodology for monitoring the work conditions of machining tools, based on expert systems that incorporate a reinforcement strategy into their knowledge base. By means of the combination of sound-processing techniques, together with the use of fuzzy-logic inference engines and hierarchization methods based on vague fuzzy numbers, it will be possible to determine existing undesirable behaviors in the machining tools, thus reducing errors, accidents and harmful failures, with consequent savings in time and costs. Aiming to show the potential for the use of this methodology, a concept test has been developed, implemented in the form of a short case study. The results obtained, even if they require more extensive validation, suggest that the methodology would allow for improving the performance and operation of machining tools, as well as the ergonomic conditions of the workplace. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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17 pages, 4328 KiB  
Article
The Fuzzy Logic in the Problems of Test Control of a Bypass Turbojet Engine Gas Generator
by Alexander Inozemtsev, Anton Petrochenkov, Vladimir Kazantsev, Igor Shmidt, Alexey Sazhenkov, Dmitry Dadenkov, Igor Gribkov and Pavel Ivanov
Mathematics 2022, 10(3), 484; https://doi.org/10.3390/math10030484 - 2 Feb 2022
Cited by 5 | Viewed by 1808
Abstract
Continuous improvement in the operational characteristics of gas turbine equipment and a significant reduction in the time of its creation have led to the development and application of new technologies for conducting research tests of a gas generator—the basic section of a bypass [...] Read more.
Continuous improvement in the operational characteristics of gas turbine equipment and a significant reduction in the time of its creation have led to the development and application of new technologies for conducting research tests of a gas generator—the basic section of a bypass turbojet engine. Carrying out such tests requires the reproduction of the thermo gas dynamic parameters of the working fluid at the gas generator inlet to ensure maximum similarity to the processes occurring in the engine being designed. Obtaining a working fluid with the required thermo gas dynamic parameters such as temperature, pressure, and air flow rate is carried out on the basis of a test complex. The test complex, as a control object, is a non-linear, non-stationary, multi-variable system, where each controlled variable substantially depends on other control actions. The article presents the main aspects of the behavior of the object under consideration, which are the basis for the development of an automated test system and, in particular, the principles of forming control algorithms based on the theory of fuzzy logic. The graphs of the state and control of the main elements of the test complex are presented. Special attention is given to the analysis of the proposed control algorithms. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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25 pages, 3423 KiB  
Article
Utilize Fuzzy Delphi and Analytic Network Process to Construct Consumer Product Design Evaluation Indicators
by Kai-Chao Yao, Jian-Yuan Lai, Wei-Tzer Huang and Jui-Che Tu
Mathematics 2022, 10(3), 397; https://doi.org/10.3390/math10030397 - 27 Jan 2022
Cited by 10 | Viewed by 4401
Abstract
In the face of an ever-changing global market, companies able to launch new products meeting consumer needs faster than their competitors may not only gain a larger market share, but also shorten the development cycle to reduce costs. However, there are currently no [...] Read more.
In the face of an ever-changing global market, companies able to launch new products meeting consumer needs faster than their competitors may not only gain a larger market share, but also shorten the development cycle to reduce costs. However, there are currently no universal design strategies and tools for evaluating the design of consumer products. Therefore, the purpose of this study is mainly to formulate a systematic and innovative product design strategy and evaluation tool, so that designers can use them to select the key factors when designing consumer products and design products that meet customer needs in the shortest development cycle. First of all, this study was designed to sort out general design methods and influencing factors in consumer product design based on theoretical analysis and expert interviews. Next, a questionnaire survey of 15 design-related experts and scholars was conducted, and the most important design methods and design factors were selected using the Fuzzy Delphi Method (FDM). After that, the analytical network process (ANP) method was used to obtain the priority weight of each design factor, and select the optimal product design strategy, QTPCP, and the deciding elements that affect consumer demand for products, including 2 dimensions, 11 design elements, and 38 design factors, making theoretical contributions to product design management. The design strategies and evaluation tools developed according to the conclusions are helpful in comprehensive planning and design selection for products of different natures, and make practical contributions, enabling product developers or designers to efficiently select the optimal product design when faced with different new product designs. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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28 pages, 3151 KiB  
Article
Learning EOQ Model with Trade-Credit Financing Policy for Imperfect Quality Items under Cloudy Fuzzy Environment
by Mahesh Kumar Jayaswal, Mandeep Mittal, Osama Abdulaziz Alamri and Faizan Ahmad Khan
Mathematics 2022, 10(2), 246; https://doi.org/10.3390/math10020246 - 13 Jan 2022
Cited by 13 | Viewed by 2031
Abstract
An imprecise demand rate creates problems in profit optimization in business scenarios. The aim is to nullify the imprecise nature of the demand rate with the help of the cloudy fuzzy method. Traditionally, all items in an ordered lot are presumed to be [...] Read more.
An imprecise demand rate creates problems in profit optimization in business scenarios. The aim is to nullify the imprecise nature of the demand rate with the help of the cloudy fuzzy method. Traditionally, all items in an ordered lot are presumed to be of good quality. However, the delivered lot may contain some defective items, which may occur during production or maintenance. Inspection of an ordered lot is indispensable in most organizations and can be treated as a type of learning. The learning demonstration, a statistical development expressing declining cost, is necessary to achieve any cyclical process. Further, defective items are sold immediately after the screening process as a single lot at a discounted price, and the fraction of defective items follows an S-shaped learning curve. The trade-credit policy is adequate for suppliers and retailers to maximize their profit during business. In this paper, an inventory model is developed with learning and trade-credit policy under the cloudy fuzzy environment where the demand rate is treated as a cloudy fuzzy number. Finally, the retailer’s total profit is maximized with respect to order quantity. Sensitivity analysis is presented to estimate the robustness of the model. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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10 pages, 708 KiB  
Article
Attribute Service Performance Index Based on Poisson Process
by Kuen-Suan Chen, Chang-Hsien Hsu and Ting-Hsin Hsu
Mathematics 2021, 9(23), 3144; https://doi.org/10.3390/math9233144 - 6 Dec 2021
Cited by 3 | Viewed by 2534
Abstract
The purpose of a shop enhancing customer satisfaction is to raise its total revenue as the rate of customer purchases in the shop increases. Some studies have pointed out that the amount of customer arrival at a shop is a Poisson process. A [...] Read more.
The purpose of a shop enhancing customer satisfaction is to raise its total revenue as the rate of customer purchases in the shop increases. Some studies have pointed out that the amount of customer arrival at a shop is a Poisson process. A simple and easy-to-use evaluation index proposed for the Poisson process with the attribute characteristic will help various shops evaluate their business performance. In addition, developing an excellent and practical service performance evaluation method will be beneficial to the advancement of shop service quality as well as corporate image, thereby increasing the profitability and competitiveness of the shop. As the surroundings of the internet of things (IoT) are becoming gradually common and mature, various commercial data measurement and collection technologies are constantly being refined to form a huge amount of production data. Efficient data analysis and application can assist enterprises in making wise and efficient decisions within a short time. Thus, following the simple and easy-to-use principle, this paper proposes an attribute service performance index based on a Poisson process. Since the index had unknown parameters, this paper subsequently figured out the best estimator and used the central limit theorem to derive the confidence interval of the service efficiency index based on random samples. Then, we constructed the membership function based on the α-cuts of the triangular shaped fuzzy number. Finally, we came up with a fuzzy testing model based on the membership function to improve the accuracy of the test when the sample size is small in order to meet enterprises’ needs for quick responses as well as reducing the evaluation cost. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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11 pages, 1709 KiB  
Article
Fuzzy Evaluation Model of Bank APP Performance Based on Circular Economy Thinking
by Tian Chen, Chun-Ming Yang, Kuen-Suan Chen and Ting-Hsin Hsu
Mathematics 2021, 9(21), 2761; https://doi.org/10.3390/math9212761 - 30 Oct 2021
Cited by 3 | Viewed by 1639
Abstract
As the environment of the Internet of Things (IoT) gradually becomes common and mature, various smart application (APP) platforms have sprung up, making what we are doing more convenient, more economical and more efficient. Then, this paper used a bank APP as the [...] Read more.
As the environment of the Internet of Things (IoT) gradually becomes common and mature, various smart application (APP) platforms have sprung up, making what we are doing more convenient, more economical and more efficient. Then, this paper used a bank APP as the research background to discuss issues related to smart APPs. Obviously, through the bank APPs, customers can complete their transfer and payment for various expenses at home, eliminating the inconvenience of going out, which not only can alleviate traffic congestion as well as reduce carbon emissions but also can save the manpower expenditure costs for banks. Consequently, improving APP performance and increasing the number of users of an APP is a very important issue. Therefore, this paper proposed an APP performance index to evaluate the performance of a bank APP. This APP performance index is to evaluate the performance of the APP through the time interval of customers’ access to the APP. The shorter the time interval is, the greater the number of users within a unit time is. In addition, based on cost considerations and effectiveness, the sample size n is usually not too large in practice, in order to make decisions quickly and accurately in a short time. Since the fuzzy testing model based on the confidence interval can be integrated with the past accumulated experience of data experts, the testing accuracy can be leveled up under the condition of small-sized samples. Accordingly, a fuzzy evaluation model was proposed to evaluate whether the performance of the bank APP can reach the required level, and this model was also regarded as a basis for decision-making to determine whether to improve the bank APP. At the same time, we can grasp the opportunities for improvement, achieve the effect of cost reduction, energy saving and carbon reduction, and further move towards the goal of innovative and intelligent management. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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13 pages, 909 KiB  
Article
A Fuzzy Evaluation Model Aimed at Smaller-the-Better-Type Quality Characteristics
by Kuen-Suan Chen and Tsun-Hung Huang
Mathematics 2021, 9(19), 2513; https://doi.org/10.3390/math9192513 - 7 Oct 2021
Cited by 7 | Viewed by 1894
Abstract
Numerous key components of tool machines possess critical smaller-the-better-type quality characteristics. Under the assumption of normality, a one-to-one mathematical relationship exists between the process quality index and the process yield. Therefore, this paper utilized the index to produce a quality fuzzy evaluation model [...] Read more.
Numerous key components of tool machines possess critical smaller-the-better-type quality characteristics. Under the assumption of normality, a one-to-one mathematical relationship exists between the process quality index and the process yield. Therefore, this paper utilized the index to produce a quality fuzzy evaluation model aimed at the small-the-better-type quality characteristics and adopted the model as a decision-making basis for improvement. First, we derived the 100(1 −α)% confidence region of the process mean and process standard deviation. Next, we obtained the 100(1 −α)% confidence interval of the quality index using the mathematical programming method. Furthermore, a one-tailed fuzzy testing method based on this confidence interval was proposed, aiming to assess the process quality. In addition, enterprises’ pursuit of rapid response often results in small sample sizes. Since the evaluation model is built on the basis of the confidence interval, not only can it diminish the risk of wrong judgment due to sampling errors, but it also can enhance the accuracy of evaluations for small sample sizes. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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22 pages, 2552 KiB  
Article
A Two Phase Integrated Fuzzy Decision-Making Framework for Green Supplier Selection in the Coffee Bean Supply Chain
by Ngoc Bao Tu Nguyen, Gu-Hong Lin and Thanh-Tuan Dang
Mathematics 2021, 9(16), 1923; https://doi.org/10.3390/math9161923 - 12 Aug 2021
Cited by 30 | Viewed by 4108
Abstract
In Vietnam, as more and more organizations are moving toward globalization, green supplier selection (GSS) has emerged as a strategic approach in supply chain management that requires supplier practices in lessening the environmental risks to society. Based on both conventional and environmental criteria, [...] Read more.
In Vietnam, as more and more organizations are moving toward globalization, green supplier selection (GSS) has emerged as a strategic approach in supply chain management that requires supplier practices in lessening the environmental risks to society. Based on both conventional and environmental criteria, this paper aims to evaluate a set of suppliers by establishing a multi-criteria decision-making (MCDM)-based framework using an integrated fuzzy analytical hierarchy process (FAHP) with the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. Initially, five GSS criteria of the environmental aspect (wastewater treatment, solid waste generation, energy consumption, air pollution, corporate social responsibility) and conventional criteria (quality, cost, delivery, and technology) are identified from the literature and consulting field experts to employ the MCDM approach. The trustworthiness of the proposed integrated framework is presented by discussing a case study in the coffee bean supply chain in Vietnam. The FAHP is used to generate criteria weights in which fuzzy set theory is applied to translate the linguistic evaluation statements of experts, and VIKOR is used to rank the alternatives against the selected criteria. From FAHP findings, the most important criteria are quantity discount, solid waste generation, order fulfillment rate, logistics cost, and purchasing cost. A consistency test is performed to ensure the uniformity of the expert’s input. The best suppliers are determined through the final ranking of the VIKOR model for the case study. The work presented provides insight to decision-makers of supplier selection that helps determine significant GSS criteria and aids in the minimization of environmental risks to society arising from the supply chain on corporate sustainability standards. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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19 pages, 7647 KiB  
Article
Bearing Fault Diagnosis Using a Grad-CAM-Based Convolutional Neuro-Fuzzy Network
by Cheng-Jian Lin and Jyun-Yu Jhang
Mathematics 2021, 9(13), 1502; https://doi.org/10.3390/math9131502 - 26 Jun 2021
Cited by 13 | Viewed by 2994
Abstract
When a machine tool is used for a long time, its bearing experiences wear and failure due to heat and vibration, resulting in damage to the machine tool. In order to make the machine tool stable for processing, this paper proposes a smart [...] Read more.
When a machine tool is used for a long time, its bearing experiences wear and failure due to heat and vibration, resulting in damage to the machine tool. In order to make the machine tool stable for processing, this paper proposes a smart bearing diagnosis system (SBDS), which uses a gradient-weighted class activation mapping (Grad-CAM)-based convolutional neuro-fuzzy network (GC-CNFN) to detect the bearing status of the machine tool. The developed GC-CNFN is composed of a convolutional layer and neuro-fuzzy network. The convolutional layer can automatically extract vibration signal features, which are then classified using the neuro-fuzzy network. Moreover, Grad-CAM is used to analyze the attention of the diagnosis model. To verify the performance of bearing fault classification, the 1D CNN (ODCNN) and improved 1D LeNet-5 (I1DLeNet) were adopted to compare with the proposed GC-CNFN. Experimental results showed that the proposed GC-CNFN required fewer parameters (20K), had a shorter average calculation time (117.7 s), and had a higher prediction accuracy (99.88%) in bearing fault classification. The proposed SBDS can not only accurately classify bearing faults, but also help users understand the current status of the machine tool. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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15 pages, 2104 KiB  
Article
A Fuzzy Seasonal Long Short-Term Memory Network for Wind Power Forecasting
by Chin-Wen Liao, I-Chi Wang, Kuo-Ping Lin and Yu-Ju Lin
Mathematics 2021, 9(11), 1178; https://doi.org/10.3390/math9111178 - 23 May 2021
Cited by 8 | Viewed by 2427
Abstract
To protect the environment and achieve the Sustainable Development Goals (SDGs), reducing greenhouse gas emissions has been actively promoted by global governments. Thus, clean energy, such as wind power, has become a very important topic among global governments. However, accurately forecasting wind power [...] Read more.
To protect the environment and achieve the Sustainable Development Goals (SDGs), reducing greenhouse gas emissions has been actively promoted by global governments. Thus, clean energy, such as wind power, has become a very important topic among global governments. However, accurately forecasting wind power output is not a straightforward task. The present study attempts to develop a fuzzy seasonal long short-term memory network (FSLSTM) that includes the fuzzy decomposition method and long short-term memory network (LSTM) to forecast a monthly wind power output dataset. LSTM technology has been successfully applied to forecasting problems, especially time series problems. This study first adopts the fuzzy seasonal index into the fuzzy LSTM model, which effectively extends the traditional LSTM technology. The FSLSTM, LSTM, autoregressive integrated moving average (ARIMA), generalized regression neural network (GRNN), back propagation neural network (BPNN), least square support vector regression (LSSVR), and seasonal autoregressive integrated moving average (SARIMA) models are then used to forecast monthly wind power output datasets in Taiwan. The empirical results indicate that FSLSTM can obtain better performance in terms of forecasting accuracy than the other methods. Therefore, FSLSTM can efficiently provide credible prediction values for Taiwan’s wind power output datasets. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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11 pages, 711 KiB  
Article
Developing a Novel Fuzzy Evaluation Model by One-Sided Specification Capability Indices
by Wei Lo, Chun-Ming Yang, Kuei-Kuei Lai, Shao-Yu Li and Chi-Han Chen
Mathematics 2021, 9(10), 1076; https://doi.org/10.3390/math9101076 - 11 May 2021
Cited by 15 | Viewed by 1857
Abstract
When all of the one-sided specification indices of each quality characteristic reach the requirements of the process quality level, they can ensure that the process capability of the product meets the requirements of the process quality level. This study constructs a fuzzy membership [...] Read more.
When all of the one-sided specification indices of each quality characteristic reach the requirements of the process quality level, they can ensure that the process capability of the product meets the requirements of the process quality level. This study constructs a fuzzy membership function based on the upper confidence limit of the index, derives the fuzzy critical value, and then labels the fuzzy critical value on the axis of the visualized radar chart as well as connects adjacent critical points to shape a regular polygonal critical region. Next, this study calculates the observed value of the index to estimate and mark it on the axis for forming a visualized fuzzy radar evaluation chart. Obviously, this fuzzy evaluation model not only reduces the testing cost but also makes the quality level quickly meet the requirements of the specifications. Further, the radar chart can reduce the risk of misjudgment attributable to sampling errors and help improve the accuracy of evaluation by a confidence-upper-limit-based fuzzy evaluation model. Therefore, this easy-to-use visualized fuzzy radar evaluation chart is used as an evaluation interface, which has good and convenient management performance to identify and improve critical-to-quality quickly. Improving the quality of the process before the product is completed will also have the advantage of reducing social losses and environmental damage costs. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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12 pages, 1545 KiB  
Article
A Fuzzy Evaluation Decision Model for the Ratio Operating Performance Index
by Mingyuan Li, Kuen-Suan Chen, Chun-Min Yu and Chun-Ming Yang
Mathematics 2021, 9(3), 262; https://doi.org/10.3390/math9030262 - 28 Jan 2021
Cited by 7 | Viewed by 1754
Abstract
In order to take into account cost and timeliness and enhance accuracy testing, this study developed the fuzzy number and membership function, using the confidence interval of ratio operating performance index. Subsequently, according to the statistical test rules and the application of the [...] Read more.
In order to take into account cost and timeliness and enhance accuracy testing, this study developed the fuzzy number and membership function, using the confidence interval of ratio operating performance index. Subsequently, according to the statistical test rules and the application of the fuzzy number and membership function, a fuzzy evaluation decision model for the operating performance index is proposed, to evaluate if the business performance reaches the needed level. Based on the abovementioned, the evaluation model in this study took into account not only timeliness but also accuracy, so that it could grasp the opportunity of improvement for operating organizations with poor operating performance after being evaluated. This fuzzy evaluation decision model for the operating performance index constructs a fuzzy membership function retrieved from an index’s confidence interval, reducing the chance of miscalculation due to sampling mistakes and improving the efficiency of evaluation. Finally, in order to facilitate the application of readers and the industry, this paper uses cases to explain the proposed fuzzy verification method. On the whole, the model proposed in this paper is a data-based auxiliary tool for the service operating performance improvement strategy. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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24 pages, 3671 KiB  
Article
A New Decision-Making Approach Based on Fermatean Fuzzy Sets and WASPAS for Green Construction Supplier Evaluation
by Mehdi Keshavarz-Ghorabaee, Maghsoud Amiri, Mohammad Hashemi-Tabatabaei, Edmundas Kazimieras Zavadskas and Arturas Kaklauskas
Mathematics 2020, 8(12), 2202; https://doi.org/10.3390/math8122202 - 10 Dec 2020
Cited by 113 | Viewed by 6332
Abstract
The construction industry is an important industry because of its effects on different aspects of human life experiences and circumstances. Environmental concerns have been considered in designing and planning processes of construction supply chains in the recent past. One of the most crucial [...] Read more.
The construction industry is an important industry because of its effects on different aspects of human life experiences and circumstances. Environmental concerns have been considered in designing and planning processes of construction supply chains in the recent past. One of the most crucial problems in managing supply chains is the process of evaluation and selection of green suppliers. This process can be categorized as a multi-criteria decision-making (MCDM) problem. The aim of this study is to propose a novel and efficient methodology for evaluation of green construction suppliers with uncertain information. The framework of the proposed methodology is based on weighted aggregated sum product assessment (WASPAS) and the simple multi-attribute rating technique (SMART), and Fermatean fuzzy sets (FFSs) are used to deal with uncertainty of information. The methodology was applied to a green supplier evaluation and selection in the construction industry. Fifteen suppliers were chosen to be evaluated with respect to seven criteria including “estimated cost”, “delivery efficiency”, “product flexibility”, “reputation and management level”, “eco-design”, and “green image pollution”. Sensitivity and comparative analyses were also conducted to assess the efficiency and validity of the proposed methodology. The analyses showed that the results of the proposed methodology were stable and also congruent with those of some existing methods. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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13 pages, 3822 KiB  
Article
Innovative Design and Fuzzy Logic Control for An Underground Moving Sieve Jig
by Deyong Shang, Zhiyuan Yang, Junjie Wang, Yuwei Wang and Yue Liu
Mathematics 2020, 8(12), 2151; https://doi.org/10.3390/math8122151 - 3 Dec 2020
Cited by 4 | Viewed by 1954
Abstract
Underground gangue filling technology in coal mines is one of the effective ways to realize green mining. In this paper, a process of underground raw coal primary selection is proposed, which is based on a mechanical sieve jig as the main washing equipment. [...] Read more.
Underground gangue filling technology in coal mines is one of the effective ways to realize green mining. In this paper, a process of underground raw coal primary selection is proposed, which is based on a mechanical sieve jig as the main washing equipment. It refers to the structure of the ground mechanical moving sieve jig. It optimizes and improves the main structure of the jig machine’s driving mechanism and gangue discharge mechanism. It meets the requirements of the technology and the narrow space environment in the underground mine and realizes the effective separation of coal and gangue. In the jigging process of a moving sieve, it is very important to keep the jig bed stable and precisely control the quantity of gangue discharge for improving the system separation accuracy and efficiency. In this paper, a control method based on a fuzzy logic combination is proposed to realize the fuzzy logic control of the motor speed of gangue discharging, which aims at the nonlinear, time-varying uncertainty and pure lag characteristics of the control system of the underground moving sieve jig. Further industrial experiments were carried out and we obtained the variation law of the gangue’s quality in the moving sieve and the output curve of the gangue motor frequency under three working conditions. The experimental results show that the fuzzy logic control algorithm can quickly stabilize the jig bed in the vibrating sieve when the quantity of gangue changes abruptly or fluctuates greatly. It improves the separation efficiency of coal and gangue and effectively solves the problems of nonlinearity, time-varying and hysteresis in the control process of the moving sieve jig. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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18 pages, 1542 KiB  
Article
Two-Tailed Fuzzy Hypothesis Testing for Unilateral Specification Process Quality Index
by Chun-Min Yu, Win-Jet Luo, Ting-Hsin Hsu and Kuei-Kuei Lai
Mathematics 2020, 8(12), 2129; https://doi.org/10.3390/math8122129 - 28 Nov 2020
Cited by 19 | Viewed by 1837
Abstract
The quality characteristics with unilateral specifications include the smaller-the-better (STB) and larger-the-better (LTB) quality characteristics. Roundness, verticality, and concentricity are categorized into the STB quality characteristics, while the wire pull and the ball shear of gold wire bonding are categorized into the LTB [...] Read more.
The quality characteristics with unilateral specifications include the smaller-the-better (STB) and larger-the-better (LTB) quality characteristics. Roundness, verticality, and concentricity are categorized into the STB quality characteristics, while the wire pull and the ball shear of gold wire bonding are categorized into the LTB quality characteristics. In terms of the tolerance, zero and infinity (∞) can be viewed as the target values in line with the STB and LTB quality characteristics, respectively. However, cost and timeliness considerations, or the restrictions of practical technical capabilities in the industry, mean that the process mean is generally far more than 1.5 standard deviations away from the target value. Researchers have accordingly proposed a process quality index conforming to the STB quality characteristics. In this study, we come up with a process quality index conforming to the LTB quality characteristics. We refer to these two types of indices as the unilateral specification process quality indices. These indices and the process yield have a one-to-one mathematical relationship. Besides, the process quality levels can be completely reflected as well. These indices possess unknown parameters. Therefore, sample data are required for calculation. Nevertheless, interval estimates can lower the misjudgment risk resulting from sampling errors more than point estimates can. In addition, considering cost and timeliness in the industry, samples are generally small, which lowers estimation accuracy. In an attempt to increase the accuracy of estimation as well as overcome the uncertainty of measured data, we first derive the confidence interval for unilateral specification process quality indices, and then propose a fuzzy membership function on the basis of the confidence interval to establish the two-tailed fuzzy testing rules for a single indicator. Lastly, we determine whether the process quality has improved. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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15 pages, 730 KiB  
Article
Advances in Tracking Control for Piezoelectric Actuators Using Fuzzy Logic and Hammerstein-Wiener Compensation
by Cristian Napole, Oscar Barambones, Isidro Calvo, Mohamed Derbeli, Mohammed Yousri Silaa and Javier Velasco
Mathematics 2020, 8(11), 2071; https://doi.org/10.3390/math8112071 - 20 Nov 2020
Cited by 14 | Viewed by 2682
Abstract
Piezoelectric actuators (PEA) are devices that are used for nano- microdisplacement due to their high precision, but one of the major issues is the non-linearity phenomena caused by the hysteresis effect, which diminishes the positioning performance. This study presents a novel control structure [...] Read more.
Piezoelectric actuators (PEA) are devices that are used for nano- microdisplacement due to their high precision, but one of the major issues is the non-linearity phenomena caused by the hysteresis effect, which diminishes the positioning performance. This study presents a novel control structure in order to reduce the hysteresis effect and increase the PEA performance by using a fuzzy logic control (FLC) combined with a Hammerstein–Wiener (HW) black-box mapping as a feedforward (FF) compensation. In this research, a proportional-integral-derivative (PID) was contrasted with an FLC. From this comparison, the most accurate was taken and tested with a complex structure with HW-FF to verify the accuracy with the increment of complexity. All of the structures were implemented in a dSpace platform to control a commercial Thorlabs PEA. The tests have shown that an FLC combined with HW was the most accurate, since the FF compensate the hysteresis and the FLC reduced the errors; the integral of the absolute error (IAE), the root-mean-square error (RMSE), and relative root-mean-square-error (RRMSE) for this case were reduced by several magnitude orders when compared to the feedback structures. As a conclusion, a complex structure with a novel combination of FLC and HW-FF provided an increment in the accuracy for a high-precision PEA. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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20 pages, 3967 KiB  
Article
A Two-Phase Model for Personnel Selection Based on Multi-Type Fuzzy Information
by Chen-Tung Chen and Wei-Zhan Hung
Mathematics 2020, 8(10), 1703; https://doi.org/10.3390/math8101703 - 3 Oct 2020
Cited by 11 | Viewed by 2519
Abstract
From the viewpoint of human resource management, personnel selection is one of the more important issues for enterprises in a high-level competitive environment. In general, many influence factors, quantitative and qualitative, affect the decision-making process of personnel selection. For considering qualitative factors, decision-makers [...] Read more.
From the viewpoint of human resource management, personnel selection is one of the more important issues for enterprises in a high-level competitive environment. In general, many influence factors, quantitative and qualitative, affect the decision-making process of personnel selection. For considering qualitative factors, decision-makers cannot always easily judge the suitable degree of each applicant. Under this situation, this research proposes a systematic decision-making method based on computing with linguistic variables. First, unsuitable applicants are filtered by considering the quantitative information of each applicant. At this stage, technique for order of preference by similarity to ideal solution (TOPSIS) and entropy methods are aggregated to eliminate unsuitable applicants in accordance with their closeness coefficient values. Second, experts (or decision-makers) use different types of 2-tuple linguistic variables to express their opinions of suitable candidates with respect to qualitative criteria. At this stage, we consider different preference functions in the preference ranking organization method for enrichment evaluation (PROMETHEE) method to calculate the outranking index of each suitable candidate. Next, we aggregate the closeness coefficient and outranking index of each suitable applicant to determine the ranking order. In order to illustrate the computational processes, an example demonstrates the practicability of the two-phase personnel selection method. The benefit of the proposed method is as follows. (1) It reduces the time for reviewing and evaluating the huge numbers of applicants. (2) It avoids subjective judgment by experts to determine the weights of all criteria. Finally, conclusions and contributions are discussed at the end of this paper. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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14 pages, 1677 KiB  
Article
Supplier Selection by Fuzzy Assessment and Testing for Process Quality under Consideration with Data Imprecision
by Kuen-Suan Chen, Tsang-Chuan Chang and Chien-Che Huang
Mathematics 2020, 8(9), 1420; https://doi.org/10.3390/math8091420 - 24 Aug 2020
Cited by 2 | Viewed by 1976
Abstract
Supply chain management models integrate upstream and downstream organizations to enable rapid response to consumer needs. For the manufacturing industry, the process quality of suppliers is thus the foundation of sustainable growth for firms and an important indicator of whether a firm can [...] Read more.
Supply chain management models integrate upstream and downstream organizations to enable rapid response to consumer needs. For the manufacturing industry, the process quality of suppliers is thus the foundation of sustainable growth for firms and an important indicator of whether a firm can effectively reduce waste and protect the environment. To this end, this paper proposes a model of supplier selection for manufacturers based on process quality assessment. First of all, Six Sigma quality index Qpk is adopted as the assessment tool to conveniently measure the quality level of process. Practical applications require estimates of Qpk from the data collected to analyze the process quality of each supplier. The fact that uncertainty is unavoidable in the collected data means that using the crisp estimate of Qpk can lead to misjudgment of the process quality. To enhance the reliability of evaluation and reduce the risk of misjudgment, the fuzzy number Q^˜pk is proposed to perform the fuzzy testing of two indices Qpk provided by suppliers with the intent of making reliable decisions on supplier selection. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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38 pages, 18092 KiB  
Article
A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering
by C. J. Luis Pérez
Mathematics 2020, 8(9), 1390; https://doi.org/10.3390/math8091390 - 19 Aug 2020
Cited by 12 | Viewed by 3286
Abstract
In Manufacturing Engineering there is a need to be able to model the behavior of technological variables versus input parameters in order to predict their behavior in advance, so that it is possible to determine the levels of variation that lead to optimal [...] Read more.
In Manufacturing Engineering there is a need to be able to model the behavior of technological variables versus input parameters in order to predict their behavior in advance, so that it is possible to determine the levels of variation that lead to optimal values of the response variables to be obtained. In recent years, it has been a common practice to rely on regression techniques to carry out the above-mentioned task. However, such models are sometimes not accurate enough to predict the behavior of these response variables, especially when they have significant non-linearities. In this present study a comparative analysis between the precision of different techniques based on conventional regression and soft computing is initially carried out. Specifically, regression techniques, based on the response surface model, as well as the use of artificial neural networks and fuzzy inference systems along with adaptive neuro-fuzzy inference systems will be employed to predict the behavior of the aforementioned technological variables. It will be shown that when there are difficulties in predicting the response parameters by using regression models, soft computing models are highly effective, being much more efficient than conventional regression models. In addition, a new method is proposed in this study that consists of using an iterative process to obtain a fuzzy inference system from a design of experiments and then using an adaptive neuro-fuzzy inference system for tuning the constants of the membership functions. As will be shown, with this method it is possible to obtain improved results in the validation metrics. The means of selecting the membership functions to develop this model from the design of experiments is discussed in this present study in order to obtain an initial solution, which will be then tuned by using an adaptive neuro-fuzzy inference system, to predict the behavior of the response variables. Moreover, the obtained results will also be compared. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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21 pages, 6291 KiB  
Article
Mobile Robot Wall-Following Control Using Fuzzy Logic Controller with Improved Differential Search and Reinforcement Learning
by Cheng-Hung Chen, Shiou-Yun Jeng and Cheng-Jian Lin
Mathematics 2020, 8(8), 1254; https://doi.org/10.3390/math8081254 - 31 Jul 2020
Cited by 20 | Viewed by 4644
Abstract
In this study, a fuzzy logic controller with the reinforcement improved differential search algorithm (FLC_R-IDS) is proposed for solving a mobile robot wall-following control problem. This study uses the reward and punishment mechanisms of reinforcement learning to train the mobile robot wall-following control. [...] Read more.
In this study, a fuzzy logic controller with the reinforcement improved differential search algorithm (FLC_R-IDS) is proposed for solving a mobile robot wall-following control problem. This study uses the reward and punishment mechanisms of reinforcement learning to train the mobile robot wall-following control. The proposed improved differential search algorithm uses parameter adaptation to adjust the control parameters. To improve the exploration of the algorithm, a change in the number of superorganisms is required as it involves a stopover site. This study uses reinforcement learning to guide the behavior of the robot. When the mobile robot satisfies three reward conditions, it gets reward +1. The accumulated reward value is used to evaluate the controller and to replace the next controller training. Experimental results show that, compared with the traditional differential search algorithm and the chaos differential search algorithm, the average error value of the proposed FLC_R-IDS in the three experimental environments is reduced by 12.44%, 22.54% and 25.98%, respectively. Final, the experimental results also show that the real mobile robot using the proposed method can effectively implement the wall-following control. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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27 pages, 1186 KiB  
Article
Apply Fuzzy DEMATEL to Explore the Decisive Factors of the Auto Lighting Aftermarket Industry in Taiwan
by Jing Li, Chi-Hui Wu, Chien-Wen Chen, Yi-Fen Huang and Ching-Torng Lin
Mathematics 2020, 8(7), 1187; https://doi.org/10.3390/math8071187 - 19 Jul 2020
Cited by 13 | Viewed by 4626
Abstract
Continuous improvement and innovation are solid foundations for the company to maintain excellent performance and competitive advantage. As the limited resources possessed by companies generally result in the incapability of implementing several improving plans simultaneously, researchers advocate that companies should evaluate the influential [...] Read more.
Continuous improvement and innovation are solid foundations for the company to maintain excellent performance and competitive advantage. As the limited resources possessed by companies generally result in the incapability of implementing several improving plans simultaneously, researchers advocate that companies should evaluate the influential relationships among key success factors (KSFs) to explore the more dominant determinants for designing improving actions. This study focused on the auto lighting aftermarket (AM) industry in which the KSFs have not yet been adequately performed to explore the decisive criteria of an improvement strategy. After a literature review and a survey of experts, a preliminary list of suitable evaluation criteria was derived. Consequently, the fuzzy and decision-making trial and evaluation laboratory (DEMATEL) method were employed to analyze and establish the causal relationship among criteria. This study contributes to the auto lighting AM industry by using a novel approach for identifying and prioritizing the KSFs. The result indicates that product integrity was the “cause” construct on the constructs of operating cost, quality and brand, technology development, and customer satisfaction. These findings contribute to help practitioners better design effective improvement strategies. Full article
(This article belongs to the Special Issue Fuzzy Applications in Industrial Engineering)
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