Browsing by Author "Chen, Toly"

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3D printing technologies for enhancing the sustainability of an aircraft manufacturing or MRO company-a multi-expert partial consensus-FAHP analysis
(2019-12-01)
Lin, Chi-Wei
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Chen, Toly
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工業工程與管理學系
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Department of Industrial Engineering and Management
The application of three-dimensional (3D) printing can enhance not only the competitiveness but also the sustainability of an aircraft manufacturing or maintenance, repair, and overhaul (MRO) company. This study sought to identify the critical factors for such applications. Herein, first, the long-term opportunities and challenges facing an aircraft manufacturing or MRO company planning to apply 3D printing technologies are discussed. The critical factors for enhancing their sustainability are identified from the discussion results. The priorities of critical factors are usually ranked using the fuzzy analytic hierarchy process (FAHP). However, existing FAHP methods cannot rank priorities reasonably when experts (or decision-makers) fail to reach an overall consensus. To address this problem, a novel partial consensus (PC)-FAHP approach is proposed that seeks to obtain a partial consensus among experts. After assessing the effectiveness of the proposed methodology with a case study involving three experts, the most critical factors were identified to be the long-term cost-effectiveness, number or types of aircraft parts that are or will be 3D printable, and advances in research and development that have been made already or are expected in the near future. In addition, the ranking result obtained using the PC-FAHP approach was different from that obtained using existing FAHP methods.
A Collaborative and Ubiquitous System for Fabricating Dental Parts Using 3D Printing Technologies
(2019-09-01)
Wang, Yu-Cheng
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Chen, Toly
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Lin, Yu-Cheng
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工業工程與管理學系
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Department of Industrial Engineering and Management
Three-dimensional (3D) printing has great potential for establishing a ubiquitous service in the medical industry. However, the planning, optimization, and control of a ubiquitous 3D printing network have not been sufficiently discussed. Therefore, this study established a collaborative and ubiquitous system for making dental parts using 3D printing. The collaborative and ubiquitous system split an order for the 3D printing facilities to fulfill the order collaboratively and forms a delivery plan to pick up the 3D objects. To optimize the performance of the two tasks, a mixed-integer linear programming (MILP) model and a mixed-integer quadratic programming (MIQP) model are proposed, respectively. In addition, slack information is derived and provided to each 3D printing facility so that it can determine the feasibility of resuming the same 3D printing process locally from the beginning without violating the optimality of the original printing and delivery plan. Further, more slack is gained by considering the chain effect between two successive 3D printing facilities. The effectiveness of the collaborative and ubiquitous system was validated using a regional experiment in Taichung City, Taiwan. Compared with two existing methods, the collaborative and ubiquitous 3D printing network reduced the manufacturing lead time by 45% on average. Furthermore, with the slack information, a 3D printing facility could make an independent decision about the feasibility of resuming the same 3D printing process locally from the beginning.
A fuzzy collaborative intelligence approach for estimating future yield with DRAM as an example
(2018-10-01)
Chen, Toly
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Wang, Yu-Cheng
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工業工程與管理學系
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Department of Industrial Engineering and Management
To increase the ecological sustainability of manufacturing, enhancing the yield of each product is a critical task that eliminates waste and increases profitability. An equally crucial task is to estimate the future yield of each product so that the majority of factory capacity can be allocated to products that are expected to have higher yields. To this end, a fuzzy collaborative intelligence (FCI) approach is proposed in this study. In this FCI approach, a group of domain experts is formed. Each expert constructs an artificial neural work (ANN) to fit an uncertain yield learning process for estimating the future yield with a fuzzy value; in past studies, however, uncertain yield learning processes were modeled only by solving mathematical programming problems. In this research, fuzzy yield estimates from different experts were aggregated using fuzzy intersection. Then, the aggregated result was defuzzified with another ANN. A real dynamic random access memory case was utilized to validate the effectiveness of the proposed methodology. According to the experimental results, the proposed methodology outperformed five existing methods in improving the estimation accuracy, which was measured in terms of the mean absolute error and the mean absolute percentage error.
A fuzzy polynomial fitting and mathematical programming approach for enhancing the accuracy and precision of productivity forecasting
(2019-06-01)
Chen, Toly
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Ou, Chungwei
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Lin, Yu-Cheng
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工業工程與管理學系
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Department of Industrial Engineering and Management
Forecasting future productivity is a critical task to every organization. However, the existing methods for productivity forecasting have two problems. First, the logarithmic or log-sigmoid value, rather than the original value, of productivity is dealt with. Second, the objective functions are not consistent with those adopted in practice. To address these problems, a fuzzy polynomial fitting and mathematical programming (FPF-MP) approach are proposed in this study. The FPF-MP approach solves two polynomial programming problems, based on the original value of productivity, in two steps to optimize accuracy and precision of forecasting future productivity, respectively. A real case was adopted to validate the effectiveness of the proposed methodology. According to the experimental results, the proposed FPF-MP approach outperformed six existing methods in improving the forecasting accuracy and precision.
A heterogeneous fuzzy collaborative intelligence approach for forecasting the product yield
(2017-08-01)
Chen, Toly
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工業工程與管理學系
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Department of Industrial Engineering and Management
For manufacturers, forecasting the future yield of a product is a critical task. However, a yield learning process involves considerable uncertainty, rendering the task difficult. Although a few fuzzy collaborative intelligence (FCI) methods have been proposed in recent years, they are not problem-free. Hence, to overcome the challenges associated with these methods and to improve the accuracy of future yield forecasts, a heterogeneous FCI approach is proposed in this study. In this method, an expert applies mathematical-programming-based or artificial-neural-network-based methods (i.e., heterogeneous methods) to model an uncertain yield learning process. Subsequently, fuzzy intersection narrows the possible range of the future yield, and finally, an artificial neural network derives a crisp (representative) value. The effectiveness of the proposed heterogeneous FCI approach was successfully demonstrated by considering data obtained from a factory manufacturing dynamic random access memory devices. The approach achieved an average increase of 21% in the forecasting accuracy compared with existing approaches. (C) 2017 Published by Elsevier B.V. All rights reserved.
A multi-granularity approach for estimating the sustainability of a factory simulation model: semiconductor packaging as an example
(2018-10-01)
Chen, Toly
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Wang, Li-Chih
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Chiu, Min-Chi
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工業工程與管理學系
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Department of Industrial Engineering and Management
Dynamic factory simulation has been considered as an effective means to control a factory. However, the large amount of money, time, efforts, and know-how required for conducting a factory simulation study force a factory to pursue the persistent application of the factory simulation model, i.e. the sustainability of the factory simulation model. Therefore, strategies are required to facilitate the rapid establishment of the factory simulation model, to lower the technical requirements of the model, and to reduce the effort and time spent on simulation tasks, thus increasing users' willingness to continue the application of the model. However, such issues have rarely been discussed. In addition, no method is available for estimating the sustainability of a factory simulation model. To address this problem, short-time evidence was analyzed rather than observing data over a long period. Then, a multi-granularity approach is proposed to estimate the sustainability of a factory simulation model based on these evidences. The proposed methodology has been applied to the simulation of a real semiconductor packaging facility. According to the experimental results, the multi-granularity approach reduced the input space by 89% and maintained a very high estimation accuracy. In addition, it also saved considerable time in building the models for estimating sustainability. Furthermore, without the multi-granularity approach, the sustainability of the factory simulation model could be observed only after a long period.
A multibelief analytic hierarchy process and nonlinear programming approach for diversifying product designs: Smart backpack design as an example
(1970-01-01)
Lin, Yu-Cheng
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Chen, Toly
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工業工程與管理學系
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Department of Industrial Engineering and Management
Owing to the cost constraint, it is difficult to incorporate all critical features into a single product design. To deal with this issue, analytic hierarchy process is a well-known method that compares the relative priorities of critical features. This study aims to illustrate that the judgment of a designer can be used to generate multiple diversified product designs. To this end, this study proposed a multibelief analytic hierarchy process and nonlinear programming approach. In the proposed methodology, a decision maker's judgment matrix is decomposed into several single-belief judgment matrices that are more consistent than the original judgment matrix and represent diversified points of view regarding the relative priorities of factors. To this end, a nonlinear programming model is established and optimized. The proposed methodology was applied to a smart backpack design problem. It was concluded that a designer's judgment was often inconsistent, which was ignored in the conventional analytic hierarchy process method but could be employed to diversify product designs.
A Piecewise Linear FGM Approach for Efficient and Accurate FAHP Analysis: Smart Backpack Design as an Example
(2020-08-01)
Wu, Hsin-Chieh
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Chen, Toly
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Huang, Chin-Hau
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工業工程與管理學系
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Department of Industrial Engineering and Management
Most existing fuzzy AHP (FAHP) methods use triangular fuzzy numbers to approximate the fuzzy priorities of criteria, which is inaccurate. To obtain accurate fuzzy priorities, time-consuming alpha-cut operations are usually required. In order to improve the accuracy and efficiency of estimating the fuzzy priorities of criteria, the piecewise linear fuzzy geometric mean (PLFGM) approach is proposed in this study. The PLFGM method estimates the alpha cuts of fuzzy priorities and then connects these alpha cuts with straight lines. As a result, the estimated fuzzy priorities will have piecewise linear membership functions that resemble the real shapes. The PLFGM approach has been applied to the identification of critical features for a smart backpack design. According to the experimental results, the PLFGM approach improved the accuracy and efficiency of estimating the fuzzy priorities of these critical features by 33% and 80%, respectively.
A type-II fuzzy collaborative forecasting approach for productivity forecasting under an uncertainty environment
(1970-01-01)
Chen, Toly
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Wang, Yu-Cheng
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Chiu, Min-Chi
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工業工程與管理學系
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Department of Industrial Engineering and Management
Forecasting factory productivity is a critical task. However, it is not easy owing to the uncertainty of productivity. Existing methods often forecast productivity using a fuzzy number. However, the range of a fuzzy productivity forecast is wide owing to the consideration of extreme cases. In this study, a fuzzy collaborative forecasting approach is proposed to forecast factory productivity using a type-II fuzzy number and by narrowing the forecast's range. The outer section of the type-II fuzzy number determines the range of productivity, while the inner section is defuzzified to derive the most likely value. Based on the experimental results, the proposed methodology surpassed existing methods in improving forecasting precision and accuracy, with a reduction in the mean absolute percentage error (MAPE) of up to 74%.
Advanced 3D printing technologies for the aircraft industry: a fuzzy systematic approach for assessing the critical factors
(2019-12-01)
Wang, Yu-Cheng
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Chen, Toly
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Yeh, Yung-Lan
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工業工程與管理學系
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Department of Industrial Engineering and Management
In the aircraft industry, three-dimensional (3D) printing can confer several benefits, such as shortened cycle times, reduced production costs, and lighter part weights. However, some concerns must be addressed for 3D-printing applications to be viable. This paper investigated these concerns by reviewing the current 3D printing practices in the aircraft industry. The literature review identified five factors critical to the applicability of advanced 3D printing technologies to the aircraft industry, and a fuzzy systematic approach was applied to assess the applicability and relative importance of the identified factors, combining fuzzy geometric mean and fuzzy analytical hierarchy process. The findings provide valuable input for countries or regions considering expanding 3D printing applications to their aircraft industries.
An advanced fuzzy approach for modeling the yield improvement of making aircraft parts using 3D printing
(2019-12-01)
Chen, Toly
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Wang, Yu-Cheng
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工業工程與管理學系
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Department of Industrial Engineering and Management
Forecasting the yield is critical for making aircraft parts using three-dimensional (3D) printing. However, the existing methods for the yield forecasting exhibit a common problem: they employ the logarithmic or log-sigmoid value, rather than the original value, of the yield to simplify the computation. To address this problem, an advanced fuzzy approach was proposed in this study. The focus of this study is to investigate the effectiveness of the advanced fuzzy approach for forecasting the yield of a 3D-printed aircraft part. The advanced fuzzy approach derived the direct-solution (DS) versions of the existing fuzzy yield learning models. These DS versions use the original yield value directly, thereby optimizing the forecasting performance. The proposed methodology was applied to the process of making an aircraft part using 3D printing to evaluate its effectiveness. Experimental results revealed significant improvements in the forecasting accuracy of the proposed methodology compared with the aforementioned methods. Furthermore, when the proposed methodology was applied to various fuzzy yield learning models, different improvements were obtained.
An advanced fuzzy collaborative intelligence approach for fitting the uncertain unit cost learning process
(2019-10-01)
Lin, Yu-Cheng
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Chen, Toly
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交大名義發表
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National Chiao Tung University
Estimating the unit cost of each product precisely and accurately is a prerequisite to determining the profitability of a manufacturer, which is usually addressed by fitting the underlying learning process. However, existing methods for this purpose often deal with a logarithmic or log-sigmoid value, rather than the original value, of the unit cost. To resolve this problem, in this study, a new fuzzy collaborative intelligence (FCI) approach is proposed by considering the original value of the unit cost directly. The effectiveness of the new FCI approach is validated with a real dynamic random access memory (DRAM) case. The experimental results showed that the new FCI approach outperformed two existing methods in improving the fitting accuracy in terms of MAE and MAPE and also in reducing the average range of the fitted unit costs.
An advanced IoT system for assisting ubiquitous manufacturing with 3D printing
(2019-08-01)
Chen, Toly
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Wang, Yu-Cheng
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工業工程與管理學系
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Department of Industrial Engineering and Management
An advanced Internet-of-things (IoT) system for assisting ubiquitous manufacturing with three-dimensional (3D) printing was designed. The system receives orders from customers on the move online and then distributes the required pieces to nearby 3D printing facilities. After the printing is completed, a freight truck visits the printing facilities sequentially to collect the printed pieces. To minimize the cycle time for delivering the order, an optimization approach that combines workload balancing and finding the shortest delivery path was proposed in this study. However, the optimization problem remained difficult; to solve this, an algorithm was developed. The effectiveness of the proposed methodology was assessed through an experiment conducted in Taichung City, Taiwan. According to the experimental results, the proposed methodology outperforms two existing methods by reducing the cycle times by an average of 33%. It also successfully balances the workloads of the 3D printing facilities, incentivizing these facilities to join the system.
An ANN approach for modeling the multisource yield learning process with semiconductor manufacturing as an example
(2017-01)
Chen, Toly
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工業工程與管理學系
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Department of Industrial Engineering and Management
Estimating the future yield of a product is a crucial task for semiconductor manufacturers. However, existing methods cannot differentiate the effects of various sources of yield improvement. To address this concern, this study proposes an innovative approach for modeling the yield learning process of a semiconductor product with artificial neural networks, which enable separating the effects of various sources of yield learning. Two real cases were used to demonstrate the effectiveness of the proposed methodology. (C) 2016 Elsevier Ltd. All rights reserved.
An ANN approach for modeling the multisource yield learning process with semiconductor manufacturing as an example
(2017-01)
Chen, Toly
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
Estimating the future yield of a product is a crucial task for semiconductor manufacturers. However, existing methods cannot differentiate the effects of various sources of yield improvement. To address this concern, this study proposes an innovative approach for modeling the yield learning process of a semiconductor product with artificial neural networks, which enable separating the effects of various sources of yield learning. Two real cases were used to demonstrate the effectiveness of the proposed methodology. (C) 2016 Elsevier Ltd. All rights reserved.
An evolving fuzzy planning mechanism for a ubiquitous manufacturing system
(2020-06-01)
Chen, Toly
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Wang, Yu-Cheng
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工業工程與管理學系
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Department of Industrial Engineering and Management
Existing ubiquitous manufacturing (UM) systems usually plan production and logistics simultaneously to enhance efficiency. A UM system can be further benefited by pursuing economical fabrication, improving product quality, and enhancing partner relationships, which involves possibly conflicting goals, but has rarely been investigated. To address this, an evolving fuzzy approach is proposed in this study by hybridizing diversifying fuzzy analytic hierarchy process (DFAHP), genetic algorithm (GA), and fuzzy technique for order preference by similarity to the ideal solution (FTOPSIS). The evolving fuzzy approach has been applied to a UM system. The superiority of the evolving fuzzy approach over existing methods in meeting various goals was successfully demonstrated by the experimental results.
An innovative fuzzy and artificial neural network approach for forecasting yield under an uncertain learning environment
(2018-08-01)
Chen, Toly
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工業工程與管理學系
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Department of Industrial Engineering and Management
Most methods for fitting an uncertain yield learning process involve using fuzzy logic and solving mathematical programming (MP) problems, and thus have several drawbacks. The present study proposed a novel fuzzy and artificial neural network (ANN) approach for overcoming these drawbacks. In the proposed methodology, an ANN is used instead of an MP model to facilitate generating feasible solutions. A two-stage procedure is established to train the ANN. The proposed methodology and several existing methods were applied to a real case in a semiconductor manufacturing factory, and the experimental results showed that the methodology outperformed the existing methods in the overall forecasting performance.
An interval fuzzy number-based fuzzy collaborative forecasting approach for DRAM yield forecasting
(1970-01-01)
Chen, Toly
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Chiu, Min-Chi
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工業工程與管理學系
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Department of Industrial Engineering and Management
Most existing fuzzy collaborative forecasting (FCF) methods adopt type-1 fuzzy numbers to represent fuzzy forecasts. FCF methods based on interval-valued fuzzy numbers (IFNs) are not widely used. However, the inner and outer sections of an IFN-based fuzzy forecast provide meaning information that serves different managerial purposes, which is a desirable feature for a FCF method. This study proposed an IFN-based FCF approach. Unlike existing IFN-based fuzzy association rules or fuzzy inference systems, the IFN-based FCF approach ensures that all actual values fall within the corresponding fuzzy forecasts. In addition, the IFN-based FCF approach optimizes the forecasting precision and accuracy with the outer and inner sections of the aggregation result, respectively. Based on the experimental results, the proposed FCF-II approach surpassed existing methods in forecasting the yield of a dynamic random access memory product.
Approximating alpha-cut operations approach for effective and efficient fuzzy analytic hierarchy process analysis
(2019-12-01)
Chen, Toly
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Lin, Yu-Cheng
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Chiu, Min-Chi
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工業工程與管理學系
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Department of Industrial Engineering and Management
Fuzzy analytic hierarchy process (FAHP) has been widely applied to multicriteria decision making (MCDM). However, deriving the fuzzy maximal eigenvalue and eigenvector of a fuzzy pairwise comparison matrix is a computationally intensive task. As a result, most existing FAHP methods estimate, rather than derive, the fuzzy maximal eigenvalue and weights. Therefore, the results are inaccurate. By contrast, the alpha-cut operations (ACO) method derives the fuzzy maximal eigenvalue and weights, but is time-consuming. To address these issues, the approximating alpha-cut operations (xACO) approach is proposed in this study. The proposed xACO approach does not enumerate all possible combinations of the a cuts of fuzzy pairwise comparison results, but approximates the membership functions of the fuzzy maximal eigenvalue and weights with logarithmic functions in the process. To evaluate the performance of the xACO approach, it was applied to two real cases. According to the experimental results, the xACO approach estimated the fuzzy maximal eigenvalue and weights effectively and efficiently based on less than 0.2% of the entire results. (C) 2019 Elsevier B.V. All rights reserved.
Assessing factors critical to smart technology applications to mobile health care - the fgm-fahp approach
(2020-06-01)
Chen, Toly
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工業工程與管理學系
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Department of Industrial Engineering and Management
Smart technologies present numerous opportunities for enhancing mobile health care. However, some concerns regarding the viability of smart technology applications must be addressed. This study investigated these concerns by reviewing the current practices of smart technology applications to mobile health care. As a result, five factors critical to the applicability of a smart technology to mobile health care are identified, and the fuzzy geometric mean-fuzzy analytic hierarchy process (FGM-FAHP) approach is proposed to assess the relative importance levels of the identified factors. The experimental results showed that the three most critical factors identified include: (a) the relaxation of the related medical laws; (b) unobtrusiveness; and (c) the precise need and situation of a user. Accordingly, approximately 44%, 26%, and 15% of the budget should be allocated to the realization of the three critical factors, respectively. In addition, the challenges involved and opportunities for enhancing the effectiveness of existing applications are discussed. (C) 2020 Fellowship of Postgraduate Medicine. Published by Elsevier Ltd. All rights reserved.