Browsing by Author "Lin, Chi-Wei"

Now showing 1 - 4 of 4

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
;
Chen, Toly
;
工業工程與管理學系
;
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 fuzzy collaborative forecasting approach considering experts' unequal levels of authority
(2020-09-01)
Chen, Tin-Chih Toly
;
Wang, Yu-Cheng
;
Lin, Chi-Wei
;
工業工程與管理學系
;
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 patent quality analysis for innovative technology and product development
(2012-01-01)
Trappey, Amy J. C.
;
Trappey, Charles V.
;
Wu, Chun-Yi
;
Lin, Chi-Wei
;
管理科學系
Enterprises evaluate intellectual property rights and the quality of patent documents in order to develop innovative products and discover state-of-the-art technology trends. The product technologies covered by patent claims are protected by law, and the quality of the patent insures against infringement by competitors while increasing the worth of the invention. Thus, patent quality analysis provides a means by which companies determine whether or not to customize and manufacture innovative products. Since patents provide significant financial protection for businesses, the number of patents filed is increasing at a fast pace. Companies which cannot process patent information or fail to protect their innovations by filing patents lose market competitiveness. Current patent research is needed to estimate the quality of patent documents. The purpose of this research is to improve the analysis and ranking of patent quality. The first step of the proposed methodology is to collect technology specific patents and to extract relevant patent quality performance indicators. The second step is to identify the key impact factors using principal component analysis. These factors are then used as the input parameters for a back-propagation neural network model. Patent transactions help judge patent quality and patents which are licensed or sold with intellectual property usage rights are considered high quality patents. This research collected 283 patents sold or licensed from the news of patent transactions and 116 patents which were unsold but belong to the technology specific domains of interest. After training the patent quality model, 36 historical patents are used to verify the performance of the trained model. The match between the analytical results and the actual trading status reached an 85% level of accuracy. Thus, the proposed patent quality methodology evaluates the quality of patents automatically and effectively as a preliminary screening solution. The approach saves domain experts valuable time targeting high value patents for R&D commercialization and mass customization of products. (C) 2011 Elsevier Ltd. All rights reserved.
An innovative yield learning model considering multiple learning sources and learning source interactions
(2019-05-01)
Chen, Tin-Chih Toly
;
Lin, Chi-Wei
;
工業工程與管理學系
;
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.