Browsing by Author "Chen, Tin-Chih Toly"

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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 Direct-solution Fuzzy Collaborative Intelligence Approach for Yield Forecasting in Semiconductor Manufacturing
(2018-01-01)
Wang, Yi-Chi
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Chen, Tin-Chih Toly
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工業工程與管理學系
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Department of Industrial Engineering and Management
Yield forecasting is a critical task to every semiconductor manufacturer. However, the existing methods for yield forecasting often deal with the logarithmic or log-sigmoid value, rather than the original value, of yield. To resolve this problem, in this study, the fuzzy collaborative intelligence (FCT) method proposed by Chen and Tin (2008) is modified, so that it can consider the original value of yield directly. The modified FCI method is called the direct-solution (DS)-FCI approach. The effectiveness oldie DS-FCI approach was validated with a real case. The experimental results showed that the DS-FCT approach outperformed Chen and Tin's FCI method in improving the forecasting accuracy and precision. (C) 2018 The Authors. Published by Elsevier B.V.
A Fuzzy Collaborative Approach for Evaluating the Suitability of a Smart Health Practice
(2019-12-01)
Chen, Tin-Chih Toly
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Wang, Yu-Cheng
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Lin, Yu-Cheng
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Wu, Hsin-Chieh
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Lin, Hai-Fen
A fuzzy collaborative approach is proposed in this study to assess the suitability of a smart health practice, which is a challenging task, as the participating decision makers may not reach a consensus. In the fuzzy collaborative approach, each decision maker first applies the alpha-cut operations method to derive the fuzzy weights of the criteria. Then, fuzzy intersection is applied to aggregate the fuzzy weights derived by all decision makers to measure the prior consensus among them. The fuzzy intersection results are then presented to the decision makers so that they can subjectively modify the pairwise comparison results to bring them closer to the fuzzy intersection results. Thereafter, the consensus among decision makers is again measured. The collaboration process will stop when no more modifications are made by any decision maker. Finally, the fuzzy weighted mean-centroid defuzzification method is applied to assess the suitability of a smart health practice. The fuzzy collaborative approach and some existing methods have been applied to assess the suitabilities of eleven smart health practices for a comparison. Among the compared practices, only the fuzzy collaborative approach could guarantee the existence of a full consensus among decision makers after the collaboration process, i.e., that the assessment results were acceptable to all decision makers.
A fuzzy collaborative forecasting approach considering experts' unequal levels of authority
(2020-09-01)
Chen, Tin-Chih Toly
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Wang, Yu-Cheng
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Lin, Chi-Wei
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工業工程與管理學系
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Department of Industrial Engineering and Management
Experts typically have unequal authority levels in collaborative forecasting tasks. Most current fuzzy collaborative forecasting methods address this problem by applying a (fuzzy) weighted average to aggregate experts' fuzzy forecasts. However, the aggregation result may be unreasonable, hence fuzzy weighted intersection operators have been proposed for fuzzy collaborative forecasting. This paper proposes that unequal expert authority levels are considered when deriving the membership function rather than the aggregation value. Therefore, the membership of a value in the aggregation result cannot exceed those in experts' fuzzy forecasts. The proposed approach was applied to forecast the yield of a dynamic random access memory product to validate its effectiveness. Experimental results showed that the proposed methodology outperformed all current best-practice methods considered in every aspect, and in particular achieving 65% mean root mean square error reduction. Thus, a high expert authority level increased the likelihood for the forecast, which could not be satisfactorily addressed by simply applying a higher weight to the forecast. (C) 2020 Elsevier B.V. All rights reserved.
A fuzzy mid-term capacity and production planning model for a manufacturing system with cloud-based capacity
(1970-01-01)
Chen, Tin-Chih 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
Most of the past cloud manufacturing (CMfg) studies investigated the short-term production planning or job scheduling of a CMfg system, while the mid-term or long-term capacity and production planning of a CMfg system has rarely been addressed. In addition, most existing methods are suitable for CMfg systems comprising three-dimensional (3D) printers, computer numerical control (CNC) machines or robots, but ignore the coordination and transportation required for moving jobs across factories. To fill these gaps, a fuzzy mid-term capacity and production planning model for a manufacturer with cloud-based capacity is proposed in this study. The proposed methodology guides a manufacturer in choosing between non-cloud-based capacity and cloud-based capacity. It can be applied to factories utilizing machines with different degrees of automation including highly automatic equipment (such as 3D printers, CNC machines, and robots) and lowly automatic (legacy) machines, while existing methods assume that orders can be easily transferred between machines that are often highly automatic. In the proposed methodology, first, various types of capacity are unequally prioritized. Then, a fuzzy mixed-integer nonlinear programming model is formulated and optimized to make the mid-term or long-term capacity and production plan of a factory. The fuzzy capacity and production planning model is designed for factories with parallel machines. The proposed methodology has been applied to a case to illustrate its applicability. According to the experimental results, the proposed methodology successfully reduced total costs by up to 8%. The advantage of the proposed methodology over existing practices in fulfilling customers' demand was also obvious.
A Partial-Consensus Posterior-Aggregation FAHP Method-Supplier Selection Problem as an Example
(2019-02-01)
Wang, Yu-Cheng
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Chen, Tin-Chih Toly
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工業工程與管理學系
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Department of Industrial Engineering and Management
Existing fuzzy analytic hierarchy process (FAHP) methods usually aggregate the fuzzy pairwise comparison results produced by multiple decision-makers (DMs) rather than the fuzzy weights estimations. This is problematic because fuzzy pairwise comparison results are subject to uncertainty and lack consensus. To address this problem, a partial-consensus posterior-aggregation FAHP (PCPA-FAHP) approach is proposed in this study. The PCPA-FAHP approach seeks a partial consensus among most DMs instead of an overall consensus among all DMs, thereby increasing the possibility of reaching a consensus. Subsequently, the aggregation result is defuzzified using the prevalent center-of-gravity method. The PCPA-FAHP approach was applied to a supplier selection problem to validate its effectiveness. According to the experimental results, the PCPA-FAHP approach not only successfully found out the partial consensus among the DMs, but also shrunk the widths of the estimated fuzzy weights to enhance the precision of the FAHP analysis.
A three-dimensional-printing-based agile and ubiquitous additive manufacturing system
(2019-02-01)
Chen, Tin-Chih 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
Mass producing items using three-dimensional (3D) printing is challenging. This study exploits this technology for mass production by developing an agile and ubiquitous additive manufacturing system based on 3D printing. The proposed system receives an order and then distributes the order among multiple 3D printing facilities to minimize the makespan (i.e., cycle time). A decision-making model was also constructed for distributing the order to multiple 3D printing facilities to minimize the makespan, and an algorithm was developed to solve the model. The effectiveness of the proposed methodology was evaluated through an experiment. The experimental results showed that the proposed methodology reduced the cycle time by 54 min on average. Additionally, it could also distribute orders and balance the workloads of multiple 3D printing facilities. The proposed system is an industrial Internet of things based technology that coordinates multiple distributed 3D printing facilities via the Internet. Its application to the mass production of 3D-printed items is efficient and economical.
Advanced aircraft manufacturing and maintenance using three-dimensional printing
(1970-01-01)
Chen, Tin-Chih Toly
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Choi, Seung-Kyum
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Moon, Seung Ki
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Wang, Yu-Cheng
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工業工程與管理學系
Advanced ambient intelligence system informatics
(2018-04-01)
Chen, Tin-Chih Toly
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工業工程與管理學系
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Department of Industrial Engineering and Management
An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach
(2020-04-01)
Chen, Tin-Chih Toly
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Wang, Yu-Cheng
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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
Current fuzzy collaborative forecasting methods have rarely considered how to determine the appropriate number of experts to optimize forecasting performance. Therefore, this study proposes an evolving partial-consensus fuzzy collaborative forecasting approach to address this issue. In the proposed approach, experts apply various fuzzy forecasting methods to forecast the same target, and the partial consensus fuzzy intersection operator, rather than the prevalent fuzzy intersection operator, is applied to aggregate the fuzzy forecasts by experts. Meaningful information can be determined by observing partial consensus fuzzy intersection changes as the number of experts varies, including the appropriate number of experts. We applied the evolving partial-consensus fuzzy collaborative forecasting approach to forecasting dynamic random access memory product yield with real data. The proposed approach forecasting performance surpassed current fuzzy collaborative forecasting that considered overall consensus, and it increased forecasting accuracy 13% in terms of mean absolute percentage error.
An incremental learning and integer-nonlinear programming approach to mining users' unknown preferences for ubiquitous hotel recommendation
(2019-07-01)
Chen, Tin-Chih Toly
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Wang, Yi-Chi
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工業工程與管理學系
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Department of Industrial Engineering and Management
To further increase the successful recommendation rate of a ubiquitous hotel recommendation system, an incremental learning and integer-nonlinear programming approach (INLP) is proposed in this study to mine users' unknown preferences. In the proposed methodology, an INLP problem is solved to adjust the values of weights in the recommendation mechanism to be closer to those in the decision-making mechanism so as to maximize the successful recommendation rate. In addition, the weights are adjusted on a rolling basis so that more historical data can be considered without inflating the INLP model. The experimental results showed that the proposed methodology outperformed several existing methods in increasing the successful recommendation rate, even with a cold start.
An innovative yield learning model considering multiple learning sources and learning source interactions
(2019-05-01)
Chen, Tin-Chih Toly
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Lin, Chi-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 yield learning models do not separate the effects of different learning sources or consider the interactions among the sources. To address this problem, a multisource-with-interaction yield learning model was developed. In this paper, the properties of this multisource yield learning model are discussed from a theoretical and practical standpoint. In this study, the proposed methodology was applied to the manufacturing process of a dynamic random access memory product. The proposed model exhibited improved accuracy in estimating the future yield, evidencing its superiority over existing yield learning models. The proposed methodology can be generalized to model the learning processes of other performance measures in manufacturing or service systems.
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
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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.
Cloud-Based Manufacturing and Service Systems
(2017-04-01)
Chen, Tin-Chih Toly
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工業工程與管理學系
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Department of Industrial Engineering and Management
Editorial: smart technologies for improving the quality of mobile health care
(2020-06-01)
Chen, Tin-Chih Toly
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Chaovalitwongse, W. Art
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O'grady, Michael J.
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Honda, Katsuhiro
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工業工程與管理學系
Evaluating the Suitability of a Smart Technology Application for Fall Detection Using a Fuzzy Collaborative Intelligence Approach
(2019-11-01)
Lin, Yu-Cheng
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Wang, Yu-Cheng
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Chen, Tin-Chih Toly
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Lin, Hai-Fen
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工業工程與管理學系
Fall detection is a critical task in an aging society. To fulfill this task, smart technology applications have great potential. However, it is not easy to choose a suitable smart technology application for fall detection. To address this issue, a fuzzy collaborative intelligence approach is proposed in this study. In the fuzzy collaborative intelligence approach, alpha-cut operations are applied to derive the fuzzy weights of criteria for each decision maker. Then, fuzzy intersection is applied to aggregate the fuzzy weights derived by all decision makers. Subsequently, the fuzzy technique for order preference by similarity to the ideal solution is applied to assess the suitability of a smart technology application for fall detection. The fuzzy collaborative intelligence approach is a posterior-aggregation method that guarantees a consensus exists among decision makers. After applying the fuzzy collaborative intelligence approach to assess the suitabilities of four existing smart technology applications for fall detection, the most and least suitable smart technology applications were smart carpet and smart cane, respectively. In addition, the ranking result using the proposed methodology was somewhat different from those using three existing methods.
Evaluating the sustainability of a smart technology application to mobile health care: the FGM-ACO-FWA approach
(2020-04-01)
Chen, Tin-Chih 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, existing applications of smart technologies to mobile health care face several difficulties. As a result, whether a smart technology application to mobile health care will be sustainable is questionable. To address this issue, the fuzzy geometric mean (FGM)-alpha-cut operations (ACO)-fuzzy weighted average (FWA) approach is proposed in this study. In the proposed methodology, at first FGM is applied to aggregate multiple experts' opinions on the relative importance of a critical factor. Then, ACO is applied to derive the absolute fuzzy importance level of the critical factor. At last, FWA is applied to assess the sustainability of the smart technology application to mobile health care. The proposed methodology has been applied to assess the sustainability of thirteen smart technology applications to mobile health care. According to the experimental results, the most and least sustainable smart technology applications to mobile health care were smart mobile service and smart clothes, respectively. In addition, the ranking result using the proposed methodology was somewhat different from those using existing methods based on approximation.
Forecasting the unit cost of a DRAM product using a layered partial-consensus fuzzy collaborative forecasting approach
(1970-01-01)
Chen, Tin-Chih Toly
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Wu, Hsin-Chieh
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交大名義發表
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National Chiao Tung University
A layered partial-consensus fuzzy collaborative forecasting approach is proposed in this study to forecast the unit cost of a dynamic random access memory (DRAM) product. In the layered partial-consensus fuzzy collaborative forecasting approach, the partial-consensus fuzzy intersection (PCFI) operator is applied instead of the prevalent fuzzy intersection (FI) operator to aggregate the fuzzy forecasts by experts. In this way, some meaningful information, such as the suitable number of experts, can be obtained through observing changes in the PCFI result when the number of experts varies. After applying the layered partial-consensus fuzzy collaborative forecasting approach to a real case, the experimental results revealed that the layered partial-consensus fuzzy collaborative forecasting approach outperformed three existing methods. The most significant advantage was up to 13%.
Fuzzy approach for production planning by using a three-dimensional printing-based ubiquitous manufacturing system
(2019-11-01)
Chen, Tin-Chih Toly
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交大名義發表
;
National Chiao Tung University
A ubiquitous manufacturing (UM) system is used in manufacturing for obtaining the Internet of things solutions and provides location-based manufacturing services. Human-induced uncertainty and early termination are two complications that hamper the effectiveness of an UM system based on three-dimensional (3D) printing. To resolve these complications, several solutions were considered in this study. First, fuzzy-valued parameters were defined to determine uncertainty. Subsequently, slack was derived to determine whether to restart an early terminated 3D printing process in the same 3D printing facility. Consequently, two optimization models - a fuzzy mixed-integer linear programming model and a fuzzy mixed-integer quadratic programming model - were developed in this study. Based on the two optimization models, a fuzzy 3D printing-based UM system that considers uncertainty and early termination was developed. The effectiveness of the proposed methodology was tested by conducting a regional experiment. The experimental results revealed that the proposed methodology could shorten the average cycle time by 9% and could enable 3D printing facilities to make real-time, online reprinting decisions.
Guaranteed-consensus posterior-aggregation fuzzy analytic hierarchy process method
(2020-06-01)
Chen, Tin-Chih Toly
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工業工程與管理學系
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Department of Industrial Engineering and Management
Current group decision-making fuzzy analytic hierarchy processes (FAHPs) have two major problems. First, inconsistent fuzzy pairwise comparison results, rather than compromised fuzzy weights, are aggregated. Second, a consensus among decision makers (DMs) cannot be guaranteed. To address these problems, in this study, the guaranteed-consensus posterior-aggregation FAHP (GCPA-FAHP) method was proposed. In the proposed methodology, the membership functions of the linguistic terms for performing fuzzy pairwise comparisons were designed to guarantee a consensus among the DMs and can be modified afterward to enhance the estimation precision. In addition, fuzzy intersection and center of gravity were used to aggregate and defuzzify the estimated fuzzy weights. The GCPA-FAHP method was applied to a real case to evaluate its effectiveness. The experimental results revealed that the GCPA-FAHP method guaranteed consensus among the DMs and improved the precision of estimating fuzzy weights.