Browsing by Author "Chiu, Min-Chi"

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A classifying ubiquitous clinic recommendation approach for forming patient groups and recommending suitable clinics
(2019-07-01)
Chen, Tin-Chih 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
Existing ubiquitous clinic recommendation systems assume that patients' preferences are alike and thus apply the same recommendation mechanism to all mobile patients. However, this assumption may be unreasonable and must be relaxed. Accordingly, this study proposes a classifying ubiquitous recommendation approach. The classifying ubiquitous recommendation approach divides patients into multiple groups by mining their unknown preferences before recommending them suitable clinics. To tune the recommendation mechanism of each patient group, an integer nonlinear programming problem is solved in a rolling manner. When a new patient accesses the ubiquitous clinic recommendation system, the recommendation mechanisms of all groups are applied to recommend the suitable clinic to the new patient. The results of a regional experiment indicated that the classifying ubiquitous recommendation approach improved the successful recommendation rate by up to 52%. Therefore, patients' unknown preferences are different and affect their behaviors in choosing clinics, which should be considered in grouping patients and in tailoring the clinic recommendation mechanism.
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 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%.
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 sustainable effectiveness of the adjustment mechanism of a ubiquitous clinic recommendation system
(2020-06-01)
Chiu, Min-Chi
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Chen, Tin-Chih Toly
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工業工程與管理學系
;
Department of Industrial Engineering and Management
Advances in computer and communication technologies have engendered opportunities for developing an improved ubiquitous health care environment. One of the crucial applications is a ubiquitous clinic recommendation system, which entails recommending a suitable clinic to a mobile patient based on his/her location, hospital department, and preferences. However, patients may not be willing or able to express their preferences. To overcome this problem, some ubiquitous clinic recommendation systems mine the historical data of patients to learn their preferences, and they apply an algorithm to adjust the recommendation algorithm after receiving more patient data. Such an adjustment mechanism may operate for several periods; however, this raises a question regarding the sustainability (i.e., long-term effectiveness) of such an adjustment mechanism. To address this question, this study modeled the improvement in the successful recommendation rate of a ubiquitous clinic recommendation system that adopts an adjustment mechanism as a learning process. Both the asymptotic value and learning speed of the learning process provide valuable information regarding the long-term effectiveness of the adjustment mechanism. The proposed methodology was applied in a regional study to a ubiquitous clinic recommendation system that adjusts the recommendation mechanism by solving an integer nonlinear programming problem on a rolling basis. The experimental results revealed that the proposed method exhibited a considerably higher level of accuracy in forecasting the successful recommendation rate compared with several existing methods. Although the adjustment mechanism exhibits long-term effectiveness, the learning speed requires improvement.
Mining the preferences of patients for ubiquitous clinic recommendation
(2020-06-01)
Chen, Tin-Chih Toly
;
Chiu, Min-Chi
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
A challenge facing all ubiquitous clinic recommendation systems is that patients often have difficulty articulating their requirements. To overcome this problem, a ubiquitous clinic recommendation mechanism was designed in this study by mining the clinic preferences of patients. Their preferences were defined using the weights in the ubiquitous clinic recommendation mechanism. An integer nonlinear programming problem was solved to tune the values of the weights on a rolling basis. In addition, since it may take a long time to adjust the values of weights to their asymptotic values, the back propagation network (BPN)-response surface method (RSM) method is applied to estimate the asymptotic values of weights. The proposed methodology was tested in a regional study. Experimental results indicated that the ubiquitous clinic recommendation system outperformed several existing methods in improving the successful recommendation rate.
Modeling an Uncertain Productivity Learning Process Using an Interval Fuzzy Methodology
(2020-06-01)
Chiu, Min-Chi
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Chen, Tin-Chih Toly
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Hsu, Keng-Wei
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工業工程與管理學系
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Department of Industrial Engineering and Management
Existing methods for forecasting the productivity of a factory are subject to a major drawback-the lower and upper bounds of productivity are usually determined by a few extreme cases, which unacceptably widens the productivity range. To address this drawback, an interval fuzzy number (IFN)-based mixed binary quadratic programming (MBQP)-ordered weighted average (OWA) approach is proposed in this study for modeling an uncertain productivity learning process. In the proposed methodology, the productivity range is divided into the inner and outer sections, which correspond to the lower and upper membership functions of an IFN-based fuzzy productivity forecast, respectively. In this manner, all actual values are included in the outer section, whereas most of the values are included within the inner section to fulfill different managerial purposes. According to the percentages of outlier cases, a suitable forecasting strategy can be selected. To derive the values of parameters in the IFN-based fuzzy productivity learning model, an MBQP model is proposed and optimized. Subsequently, according to the selected forecasting strategy, the OWA method is applied to defuzzify a fuzzy productivity forecast. The proposed methodology has been applied to the real case of a dynamic random access memory factory to evaluate its effectiveness. The experimental results indicate that the proposed methodology was superior to several existing methods, especially in terms of mean absolute error, mean absolute percentage error, and root mean square error in evaluating the forecasting accuracy. The forecasting precision achieved using the proposed methodology was also satisfactory.