Browsing by Author "Chiang, Tzu-An"

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A DEA benchmarking methodology for new product development process optimization
(2007)
Trappey, Amy J. C.
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Chiang, Tzu-An
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Chen, Wen-Chih
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Kuo, Jen-Yau
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Yu, Chia-Wei
Developing new products on time within budget constraints is a crucial issue to survive in today\'s competitive marketplace. However, unpredictable incidents occur during new product development (NPD) processes, which often cause expenses, resources and schedule overruns. Traditional project management tools lack of efficient and effective methods to solve these problems and challenges. Hence, this study applies the data envelopment analysis (DEA) concept to develop a novel project planning and management decision support methodology for NPD that can optimally allocate resources and dynamically response to unexpected delays and budget overruns. The research adopts the methodology to a mobile phone NPD project case to demonstrate the method\'s real-world application and illustrate the effectiveness of the proposed methodology in-depth.
A DEA benchmarking methodology for strategic management of new product development based on decentralized profit-center business model
(2007-01-01)
Trappey, Amy J. C.
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Chiang, Tzu-An
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Wu, Hsin-Ying
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Hou, Eric J. -R.
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管理科學系
In today's highly competitive marketplace, technology-driven large organizations widely adopt decentralized profit-center business model. In order to complete a series of NPD activities on time and in budget, the profit-center managers need an objective benchmarking approaches to gain precious perception into the relation of different resource allocations, profits, costs and times for each NPD activity. Thus, this study put forward a novel benchmarking methodology to optimize the NPD activities within a profit center for achieving the maximum profit goal and to guide NPD engineers to learn the best benchmarking practices. By applying the real case of the electric scooter design project, this research demonstrates the methodology at work with superior results, comparing to other existing approaches.
A Genetic Algorithm for Integration Planning of Assembly Operation and Machine Layout
(2010)
Chiang, C. J.
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Che, Z. H.
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Chiang, Tzu-An
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Che, Zhen-Guo
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資訊管理與財務金融系註：原資管所+財金所
This study is intended to examine the issue regarding the integration of assembly operations and machine layouts. For this issue, we consider a scenario of multiple orders and therefore build up an optimized mathematical model of synchronous planning. In the study, we develop an optimization mathematical model that uses a genetic algorithm (GA) for finding solutions and identify the most proper parameter value that best fit the GA by means of the experimental design. Ultimately, we use a case for a methodological application and the result shows that the GA may effectively integrate assembly operations and machine layouts for finding solutions.
A strategic product portfolio management methodology considering R&D resource constraints for engineering-to-order industries
(2009)
Trappey, Charles V.
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Trappey, Amy J. C.
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Chiang, Tzu-An
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Kuo, Jen-Yau
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管理科學系
New product development Projects Usually incur high cost and high risk for corporations. For engineering-to-order industries, where each order requires a certain degree of new product design and customisation, R&D resources must be properly utilised for the highest possible profits. Thus, Engineering-To-Order firms often search for optimal product portfolios that effectively allocate limited enterprise resources to projects with the greatest potential for success. This paper develops a Product Portfolio Management (PPM) methodology that systematically analyses product portfolio strategies at the beginning of the product life cycle. Multiattribute decision analysis estimates the performance values used to construct plausible product portfolios during the early Engineering-To-Order design stage. An activity group concept evaluates resource requirements and the potential risks of product portfolios. The revenue of a product portfolio considers the levels Of Substitution among products. Finally, an industrial computer manufacturer, that is, a typical Engineering-To-Order company, adopts the methodology for its PPM to demonstrate the practical application of the research.
An Intelligent System for Automated Binary Knowledge Document Classification and Content Analysis
(2011)
Chiang, Tzu-An
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Wu, Chun-Yi
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Trappey, Charles V.
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Trappey, Amy J. C.
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交大名義發表
Many companies rely on patent engineers to search patent documents and offer recommendations and advice to R&D engineers. Given the increasing number of patent documents filed each year, new means to effectively and efficiently identify and manage technology specific patent documents are required. This research applies a back-propagation artificial neural network (BPANN), a hierarchical ontology technique, and a normalized term frequency (NTF) method to develop an intelligent system for binary knowledge document classification and content analysis. The intelligent system minimizes inappropriate patent document classification and reduces the effort required to search and screen patents for analysis. Finally, this paper uses the design of light emitting diode (LED) lamps as a case study to illustrate and verify the efficiency of automated binary knowledge document classification and content analysis.
FEED-FORWARD NEURAL NETWORKS TRAINING: A COMPARISON BETWEEN GENETIC ALGORITHM AND BACK-PROPAGATION LEARNING ALGORITHM
(2011-10-01)
Che, Zhen-Guo
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Chiang, Tzu-An
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Che, Zhen-Hua
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
This study discusses the advantages and characteristics of the genetic algorithm and back-propagation neural network to train a feed-forward neural network to cope with weighting adjustment problems. We compare the performances of a back-propagation neural network and genetic algorithm in the training outcomes of three examples by referring to the measurement indicators and experiment data. The results show that the back-propagation neural network is superior to the genetic algorithm. Also, the back-propagation neural network has faster training speed than the genetic algorithm. However, the back-propagation neural network has the shortcoming of overtraining, while the genetic algorithm does not. The experiment result proves that the back-propagation neural network yields better outcomes than the genetic algorithm.
Ontology-based neural network for patent knowledge management in design collaboration
(2013-04-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Chiang, Tzu-An
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Huang, Yi-Hsuan
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管理科學系
In order to stimulate innovation during the collaborative process of new product and production development, especially to avoid duplicating existing techniques or infringing upon others' patents and intellectual property rights, the collaborative team of research and development, and patent engineers must accurately identify relevant patent knowledge in a timely manner. This research develops a novel knowledge management approach using ontology-based artificial neural network (ANN) algorithm to automatically classify and search knowledge documents stored in huge online patent corpuses. This research focuses on developing a smart and semantic oriented classification and search from the sources of the most critical and well-structured knowledge publications, i.e. patents, to gain valuable and practical references for the collaborative networks of technology-centric product and production development teams. The research uses the domain ontology schema created using Protege and derives the semantic concept probabilities of key phrases that frequently occur in domain relevant patent documents. Then, by combining the term frequencies and the concept probabilities of key phrases as the ANN inputs, the method shows significant improvement in classification accuracy. In addition, this research provides an advanced semantic-oriented search algorithm to accurately identify related patent documents in the patent knowledge base. The case demonstration analyses 343 chemical mechanical polishing and 150 radio-frequency identification patents sample sets to verify and measure the performance of the proposed approach. The results are compared with the previous automatic classification methods demonstrating much improved outcomes.
Using analytic network process and turbo particle swarm optimization algorithm for non-balanced supply chain planning considering supplier relationship management
(2012-08-01)
Che, Z. H.
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Chiang, Tzu-An
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Che, Zhen-Guo
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
"Supply chain planning has been regarded as a key strategic decision-making activity for the enterprises under the current business circumstances. From the point of supply chain planning, the important issues are to find suitable and quality partners and to decide upon an appropriate production-distribution plan. In this study, hence, we address to develop a decision methodology for supply chain planning in multi-echelon non-balanced supply chain system, taking into account such four criteria as cost, quality, delivery and supplier relationship management and considering quantity discount and capacity constraints. The proposed methodology is based on the analytic network process and turbo particle swarm optimization (TPSO), to evaluate partners and to determine an optimal supply chain network pattern and production-distribution mode. Finally, to demonstrate the performance of the proposed TPSO algorithm, comparative numerical experiments are performed by TPSO, particle swarm optimization (PSO) and genetic algorithm (GA). Empirical analysis results demonstrate that TPSO can outperform PSO and GA in non-balanced supply chain planning problems."