進階搜尋


   電子論文尚未授權公開,紙本請查館藏目錄
(※如查詢不到或館藏狀況顯示「閉架不公開」,表示該本論文不在書庫,無法取用。)
系統識別號 U0026-2506201812392200
論文名稱(中文) 基於後偏好表達方法的產品造型設計模式之研究
論文名稱(英文) Research on Product Form Design Model Based on a Posterior Preference Articulation Approach
校院名稱 成功大學
系所名稱(中) 工業設計學系
系所名稱(英) Department of Industrial Design
學年度 106
學期 2
出版年 107
研究生(中文) 李永鋒
研究生(英文) Yongfeng Li
學號 P38033014
學位類別 博士
語文別 英文
論文頁數 89頁
口試委員 召集委員-林振陽
指導教授-謝孟達
口試委員-蕭世文
口試委員-蔡宏政
口試委員-楊智傑
口試委員-徐芳真
中文關鍵字 產品造型設計  感性工學  決策  後偏好表達方法  多目標進化演算法 
英文關鍵字 Product form design  Kansei engineering  Decision making  Posterior preference articulation approach  Multi-objective evolutionary algorithm 
學科別分類
中文摘要 與消費者感性需求相關聯的情感反應在以消費者為中心的研究中越來越受到重視,為了設計能夠吸引消費者的產品,設計師應該考慮多情感反應。設計產品以滿足多情感反應屬於多目標優化問題,然而現有的研究經常將多目標優化問題轉換為單目標優化問題,從而獲得唯一的最優解,這限制了多目標優化設計對設計師或消費者的作用。
為了有效地進行多目標情感反應的產品造型優化設計,本文提出一種基於後偏好表達方法的設計模式,該模式以感性工學理論為基礎,將多目標優化與多準則決策相結合,先產生一系列帕累托最優解,設計師或消費者根據自己的情感反應偏好對這些帕累托最優解進行權衡,從中選擇一個最優解。在研究過程中,首先,進行設計分析,以明確設計變數和情感反應。接著,採用數量化理論Ⅰ類建立情感反應與設計變數之間的關係模型,在此基礎上構建同時最優化所有情感反應的多目標優化模型。然後,採用三種有代表性的多目標進化演算法,即非支配排序遺傳演算法II、基於帕累托包絡的選擇演算法II、強度帕累托進化演算法2,求解該多目標優化模型,並對這三種多目標進化演算法進行比較,以確定最適於感性工學的多目標進化演算法。最後,將模糊層次分析法和灰色關聯分析法相結合,從帕累托最優解集中選擇最能滿足消費者多情感反應偏好的產品造型設計方案,並對所得到的最優設計方案進行驗證。
本文以汽車造型設計為例進行研究,結果顯示,在三種多目標進化演算法中強度帕累托進化演算法2是最適合於感性工學的多目標進化演算法,模糊層次分析法和灰色關聯分析法的結合適合於依據情感反應的主觀性較強的帕累托最優解集中設計方案的決策問題,本文所提出的基於後偏好表達方法的產品造型設計模式在獲取最優設計時是正確有效的,可作為一種通用的多情感反應產品造型優化設計模式。
英文摘要 Affective responses concern consumers’ affective needs and have received increasing attention in consumer-centered research. To design a product that appeals to consumers, designers should consider multiple affective responses. Designing products capable of satisfying multiple affective responses falls into the category of multi-objective optimization (MOO). However, most existing approaches transform multiple objectives into a single objective during optimization, which limits their usefulness to designers or consumers.
This dissertation proposes a posterior preference articulation approach to effectively optimize product form design concerning multiple affective responses. This approach is based on Kansei engineering theory, and combines MOO with multi-criteria decision making (MCDM). It initially generates many Pareto optimal solutions, and then designers or consumers make trade-off decisions to select the best solution from these Pareto optimal solutions according to their preferences for multiple affective responses. In the research process, design analysis is first used to identify design variables and multiple affective responses. Subsequently, quantification theory type I is used to build the relationship models between multiple affective responses and design variables; an MOO model that involves optimizing multiple affective responses simultaneously is then constructed based on these models. After that, to solve this MOO model, three representative multi-objective evolutionary algorithms (MOEAs) are applied which include non-dominated sorting genetic algorithm-II (NSGA-II), Pareto envelope-based selection algorithm-II (PESA-II), and strength Pareto evolutionary algorithm 2 (SPEA2), and then comparison of the three MOEAs is made to determine the most suitable MOEA for Kansei engineering. Finally, the combination of fuzzy analytic hierarchy process (FAHP) and grey relational analysis (GRA) is employed to select the best product form design solution from the generated Pareto optimal set in accordance with the consumers’ preferences related to multiple affective responses; and the obtained best design solution is verified.
Case study involving the design of car form was conducted. Results suggest that among the three MOEAs, SPEA2 is the most suitable MOEA for Kansei engineering. In addition, the combination of FAHP and GRA can be used to select the best solution from Pareto optimal set according to multiple affective responses, which is a decision process involved many subjective factors. Moreover, the proposed product form design mode based on posterior articulation approach is valid and effective in obtaining the optimal design, and can be used as a universal mode for optimizing product form design concerning multiple affective responses.
論文目次 摘要 ii
SUMMARY iii
ACKNOWLEDGEMENTS v
TABLE OF CONTENTS vi
LIST OF TABLES ix
LIST OF FIGURES x
LIST OF SYMBOLS AND ABBREVIATIONS xii
CHAPTER 1 INTRODUCTION 1
1.1 Research Background 1
1.2 Problem Statements 2
1.3 Research Objectives 4
1.4 Research Scope and Limitations 5
1.5 Organization of the Dissertation 6
CHAPTER 2 LITERATURE REVIEW 8
2.1 Kansei Engineering 8
2.2 Posterior Preference Articulation Approach 8
2.3 Multi-objective Optimization 10
2.4 Multi-objective Evolutionary Algorithm 13
2.5 Multi-criteria Decision Making 16
CHAPTER 3 PROPOSED METHODOLOGY 18
3.1 Framework of the Proposed Methodology 18
3.2 Design Analysis 18
3.2.1 Design Variables Analysis 18
3.2.2 Affective Responses Analysis 19
3.3 MOO Model Construction for Product Form Design 20
3.3.1 Building Predictive Models for Multiple Affective Responses 20
3.3.2 MOO Model Construction 21
3.4 Pareto Optimal Solutions Generation Using MOEAs 21
3.4.1 NSGA-II 21
3.4.2 PESA-II 24
3.4.3 SPEA2 26
3.5 Best Solution Selection Using MCDM Methods 30
3.5.1 Deriving Preference Weights Using FAHP 30
3.5.2 Obtaining Best Design Solution Using GRA 34
CHAPTER 4 COMPARISON OF MOEAS IN PRODUCT FORM DESIGN 37
4.1 Outline of the Comparison of MOEAs in Product Form Design 37
4.2 Design Analysis 38
4.2.1 Design Variables Analysis 38
4.2.2 Affective Responses Analysis 42
4.3 Construction of MOO Model for Product Form Design 45
4.3.1 Designing Experimental Samples 45
4.3.2 Collecting Experimental Data 47
4.3.3 Building Predictive Models Using QT1 49
4.3.4 MOO Model Construction 49
4.4 Optimizing Product Alternatives Using MOEAs 54
4.5 Results and Discussion 55
4.5.1 Comparison of Performance Metrics of MOEAs 55
4.5.2 Cluster Analysis for the Obtained Pareto Optimal Solutions 58
4.5.3 Application of MOEAs in Product Form Design 63
CHAPTER 5 A POSTERIOR PREFERENCE ARTICULATION APPROACH USING THE COMBINATION OF MOO AND MCDM 65
5.1 Outline of the Proposed Approach 65
5.2 Implementation Procedures 66
5.2.1 Design Analysis and MOO Model Construction 66
5.2.2 Pareto Optimal Solutions Generation Using SPEA2 66
5.2.3 Deriving Preference Weights Using FAHP 68
5.2.4 Obtaining Best Design Solution Using GRA 69
5.2.5 Verification Result 72
5.3 Discussion 74
5.3.1 Integration of Multi-objective Optimization and Multi-criteria Decision Making 74
5.3.2 Application of the Posterior Preference Articulation Approach 75
CHAPTER 6 CONCLUSIONS AND SUGGESTIONS 78
6.1 Overview of Conclusions 78
6.2 Suggestions for the Follow-up Research Studies 79
REFERENCES 80
Appendix A PUBLICATIONS 89
A.1 Journal Papers 89
A.2 Conference Paper 89
VITA 90
參考文獻 Ahmadi, M. H., Ahmadi, M. A., Bayat, R., Ashouri, M., & Feidt, M. (2015). Thermo-economic optimization of Stirling heat pump by using non-dominated sorting genetic algorithm. Energy Conversion and Management, 91, 315-322.
Ahmadi, M. H., Sayyaadi, H., Mohammadi, A. H., & Barranco-Jimenez, M. A. (2013). Thermo-economic multi-objective optimization of solar dish-Stirling engine by implementing evolutionary algorithm. Energy Conversion and Management, 73, 370-380.
Alonso, J. A., & Lamata, M. T. (2006). Consistency in the analytic hierarchy process: a new approach. International journal of uncertainty, fuzziness and knowledge-based systems, 14(4), 445-459.
Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2-3), 191-203.
Brunelli, M. (2015). Introduction to the Analytic Hierarchy Process. New York: Springer.
Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233-247.
Bui, L. T., & Alam, S. (2008). Multi-objective optimization in computational intelligence: theory and practice. Hershey, PA: IGI Global.
Camargo, M., Wendling, L., & Bonjour, E. (2014). A fuzzy integral based methodology to elicit semantic spaces in usability tests. International Journal of Industrial Ergonomics, 44(1), 11-17.
Chang, D.-Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649-655.
Chen, C.-C., & Chuang, M.-C. (2008). Integrating the Kano model into a robust design approach to enhance customer satisfaction with product design. International Journal of Production Economics, 114(2), 667-681.
Chen, H.-Y., & Chang, Y.-M. (2009). Extraction of product form features critical to determining consumers’ perceptions of product image using a numerical definition-based systematic approach. International Journal of Industrial Ergonomics, 39(1), 133-145.
Chen, H.-Y., Chang, Y.-M., & Tung, T.-C. (2014). Comparison of Two Quantitative Analysis Techniques to Predict the Evaluation of Product Form Design. Mathematical Problems in Engineering, 2014, Article ID 989382, 9 pages.
Chen, K.-M., Chou, Y.-P., & Yang, M.-Y. (2010). Developing a System for Deriving Aesthetic Product Shapes Based on Shape Grammar and Kansei Information-the Case of Kettles. International Journal of Kansei Information, 1(1), 17-30.
Cluzel, F., Yannou, B., & Dihlmann, M. (2012). Using evolutionary design to interactively sketch car silhouettes and stimulate designer's creativity. Engineering Applications of Artificial Intelligence, 25(7), 1413-1424.
Coello, C. A. C. (2006). Evolutionary multi-objective optimization: A historical view of the field. IEEE Computational Intelligence Magazine, 1(1), 28-36.
Coello, C. A. C., Dhaenens, C., & Jourdan, L. (2010). Advances in Multi-Objective Nature Inspired Computing. Berlin Heidelberg: Springer.
Coello, C. A. C., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems (2nd ed.). New York, NY: Springer.
Cohon, J. L., & Marks, D. H. (1975). A review and evaluation of multiobjective programing techniques. Water Resources Research, 11(2), 208-220.
Corne, D. W., Jerram, N. R., Knowles, J. D., & Oates, M. J. (2001). PESA-II: Region-based Selection in Evolutionary Multiobjective Optimization. Paper presented at the Proceedings of the Genetic and Evolutionary Computation Conference, San Francisco.
Corne, D. W., Knowles, J. D., & Oates, M. J. (2000). The Pareto envelope-based selection algorithm for multiobjective optimization. Paper presented at the Parallel Problem Solving from Nature PPSN VI.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. Chichester: John Wiley & Sons.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.
Deng, J. (1989). Introduction to grey system theory. The Journal of grey system, 1(1), 1-24.
Dore, C., & Murphy, M. (2014). Semi-automatic generation of as-built BIM façade geometry from laser and image data. Journal of Information Technology in Construction, 19(2), 20-46.
Durillo, J. J., & Nebro, A. J. (2011). jMetal: A Java framework for multi-objective optimization. Advances in Engineering Software, 42(10), 760-771.
Goh, C.-K., & Tan, K. C. (2009). A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Transactions on Evolutionary Computation, 13(1), 103-127.
Gu, Z., Xi Tang, M., & Frazer, J. H. (2006). Capturing aesthetic intention during interactive evolution. Computer-Aided Design, 38(3), 224-237.
Guo, F., Liu, W. L., Liu, F. T., Wang, H., & Wang, T. B. (2014). Emotional design method of product presented in multi-dimensional variables based on Kansei Engineering. Journal of Engineering Design, 25(4-6), 194-212.
Hadka, D. (2016). Beginner's Guide to the MOEA Framework. North Charleston: CreateSpace Independent Publishing Platform.
Härdle, W. K., & Simar, L. (2015). Applied multivariate statistical analysis (4th ed.). Berlin, Heidelberg: Springer.
Hsiao, S.-W., & Chen, C.-H. (1997). A semantic and shape grammar based approach for product design. Design Studies, 18(3), 275-296.
Hsiao, S.-W., Chiu, F.-Y., & Lu, S.-H. (2010). Product-form design model based on genetic algorithms. International Journal of Industrial Ergonomics, 40(3), 237-246.
Hsiao, S.-W., Hsu, C.-F., & Tang, K.-W. (2013). A consultation and simulation system for product color planning based on interactive genetic algorithms. Color Research & Application, 38(5), 375-390.
Hsiao, S.-W., & Ko, Y.-C. (2013). A study on bicycle appearance preference by using FCE and FAHP. International Journal of Industrial Ergonomics, 43(4), 264-273.
Hsiao, S.-W., & Tsai, H.-C. (2005). Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design. International Journal of Industrial Ergonomics, 35(5), 411-428.
Huang, S.-H. S., & Hsu, W.-K. K. (2016). An assessment of service quality for international distribution centers in Taiwan - a QFD approach with fuzzy AHP. Maritime Policy & Management, 43(4), 509-523.
Hyun, K. H., Lee, J.-H., Kim, M., & Cho, S. (2015). Style synthesis and analysis of car designs for style quantification based on product appearance similarities. Advanced Engineering Informatics, 29(3), 483-494.
Igel, C., Hansen, N., & Roth, S. (2007). Covariance matrix adaptation for multi-objective optimization. Evolutionary computation, 15(1), 1-28.
Ishizaka, A., & Nemery, P. (2013). Multi-criteria decision analysis: methods and software. Chichester, West Sussex: Wiley.
Jiang, H., Kwong, C. K., Liu, Y., & Ip, W. H. (2015). A methodology of integrating affective design with defining engineering specifications for product design. International Journal of Production Research, 53(8), 2472-2488.
Jiang, S., Ong, Y.-S., Zhang, J., & Feng, L. (2014). Consistencies and contradictions of performance metrics in multiobjective optimization. IEEE Transactions on Cybernetics, 44(12), 2391-2404.
Jiao, L., Gong, M., Ma, W., & Shang, R. (2008). Multi-Objective Optimization Using Artificial Immune Systems. In L. T. Bui & S. Alam (Eds.), Multi-Objective Optimization in Computational Intelligence: Theory and Practice (pp. 106-147). Hershey, PA: IGI Global.
Kahraman, C. (2008). Fuzzy multi-criteria decision making: theory and applications with recent developments. New York, NY: Springer Science & Business Media.
Kim, J.-O., & Mueller, C. W. (1978). Factor analysis: Statistical methods and practical issues. Newbury Park: Sage Publication.
Klir, G., & Yuan, B. (1995). Fuzzy sets and fuzzy logic. New Jersey: Prentice Hall.
Kumar, R., & Singh, P. K. (2010). Assessing solution quality of biobjective 0-1 knapsack problem using evolutionary and heuristic algorithms. Applied Soft Computing, 10(3), 711-718.
Kwong, C. K., & Bai, H. (2002). A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment. Journal of intelligent manufacturing, 13(5), 367-377.
Lai, H.-H., Chang, Y.-M., & Chang, H.-C. (2005). A robust design approach for enhancing the feeling quality of a product: a car profile case study. International Journal of Industrial Ergonomics, 35(5), 445-460.
Lai, H.-H., Chen, C.-H., Chen, Y.-C., Yeh, J.-W., & Lai, C. F. (2009). Product design evaluation model of child car seat using gray relational analysis. Advanced Engineering Informatics, 23(2), 165-173.
Lee, A. R. (1995). Application of modified fuzzy AHP method to analyze bolting sequence of structural joints. Lehigh University, Bethlehem, PA, USA.
Lee, D.-H., Jeong, I.-J., & Kim, K.-J. (2009). A posterior preference articulation approach to dual-response-surface optimization. IIE Transactions, 42(2), 161-171.
Lee, D.-H., Kim, K.-J., & Koksalan, M. (2011). A posterior preference articulation approach to multiresponse surface optimization. European Journal of Operational Research, 210(2), 301-309.
Lee, S., Stappers, P. J., & Harada, A. (1999). Extending of Design approach based on Kansei by Dynamic Manipulation of 3D Objects. Paper presented at the Bulletin of 4th Asian Design Conference.
Li, Y., & Zhu, L. (2017). Optimisation of product form design using fuzzy integral-based Taguchi method. Journal of Engineering Design, 28(7-9), 480-504.
Lu, W., & Petiot, J.-F. (2014). Affective design of products using an audio-based protocol: Application to eyeglass frame. International Journal of Industrial Ergonomics, 44(3), 383-394.
Marler, R. T., & Arora, J. S. (2004). Survey of multi-objective optimization methods for engineering. Structural and multidisciplinary optimization, 26(6), 369-395.
Matsubara, Y., & Nagamachi, M. (1997). Hybrid Kansei Engineering System and design support. International Journal of Industrial Ergonomics, 19(2), 81-92.
Nagamachi, M. (1995). Kansei Engineering: A new ergonomic consumer-oriented technology for product development. International Journal of Industrial Ergonomics, 15(1), 3-11.
Nagamachi, M. (2002). Kansei engineering as a powerful consumer-oriented technology for product development. Applied Ergonomics, 33(3), 289-294.
Nagamachi, M. (2011). Kansei/affective engineering. Boca Raton, FL: CRC Press.
Nagamachi, M., & Lokman, A. M. (2011). Innovations of Kansei engineering. Boca Raton, FL: CRC Press.
Nunnally, J. C. (1967). Psychometric theory. New York: McGraw-Hill.
Padhye, N., & Deb, K. (2011). Multi-objective optimisation and multi-criteria decision making in SLS using evolutionary approaches. Rapid Prototyping Journal, 17(6), 458-478.
Pal, D., Mahapatra, G. S., & Samanta, G. P. (2015). Stability and bionomic analysis of fuzzy parameter based prey–predator harvesting model using UFM. Nonlinear Dynamics, 79(3), 1939-1955.
Pervez, H., Mozumder, M. S., & Mourad, A.-H. I. (2016). Optimization of injection molding parameters for HDPE/TiO2nanocomposites fabrication with multiple performance characteristics using the Taguchi method and grey relational analysis. Materials, 9(8), 1-12.
Prasad, K. S., Chalamalasetti, S. R., & Damera, N. R. (2015). Application of grey relational analysis for optimizing weld bead geometry parameters of pulsed current micro plasma arc welded inconel 625 sheets. International Journal of Advanced Manufacturing Technology, 78(1-4), 625-632.
Ruiz-Montiel, M., Boned, J., Gavilanes, J., Jimenez, E., Mandow, L., & Perez-de-la-Cruz, J. L. (2013). Design with shape grammars and reinforcement learning. Advanced Engineering Informatics, 27(2), 230-245.
Saaty, T. L. (1980). The analytic hierarchy process: planning, priority setting, resources allocation. New York: McGraw.
Salmasnia, A., Moeini, A., Mokhtari, H., & Mohebbi, C. (2013). A robust posterior preference decision-making approach to multiple response process design. International Journal of Applied Decision Sciences, 6(2), 186-207.
Schott, J. R. (1995). Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. Massachusetts Institute of Technology, Boston, MA.
Shieh, M.-D., Li, Y., & Yang, C.-C. (2017). Product Form Design Model Based on Multiobjective Optimization and Multicriteria Decision-Making. Mathematical Problems in Engineering, 2017, Article ID 5187521, 15 pages.
Shieh, M.-D., Li, Y., & Yang, C.-C. (2018). Comparison of multi-objective evolutionary algorithms in hybrid Kansei engineering system for product form design. Advanced Engineering Informatics, 36, 31-42.
Stiny, G. (1980). Introduction to shape and shape grammars. Environment and planning B: planning and design, 7(3), 343-351.
Stiny, G., & Gips, J. (1972). Shape Grammars and the Generative Specification of Painting and Sculpture. Paper presented at the Information Processing 71, Amsterdam.
Su, J.-N., Zhang, Q.-W., Wu, J.-H., & Liu, Y. (2014). Evoluationary design of product multi-image styling. Computer Integrated Manufacturing Systems, 20(11), 2675-2682.
Sutono, S. B., Abdul-Rashid, S. H., Aoyama, H., & Taha, Z. (2016). Fuzzy-based Taguchi method for multi-response optimization of product form design in Kansei engineering: a case study on car form design. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 10(9), 1-16.
Taguchi, G., Chowdhury, S., & Wu, Y. (2005). Taguchi's quality engineering handbook. Hoboken, New Jersey: John Wiley & Sons, Inc.
Tjalve, E. (1979). A short course in industrial design. London: Newnes-Butterworths.
Tzeng, G.-H., & Huang, J.-J. (2011). Multiple attribute decision making: methods and applications. Boca Raton, FL: CRC press.
Van Laarhoven, P. J. M., & Pedrycz, W. (1983). A fuzzy extension of Saaty's priority theory. Fuzzy Sets and Systems, 11(1), 229-241.
Van Veldhuizen, D. A., & Lamont, G. B. (1998). Multiobjective evolutionary algorithm research: A history and analysis. Department of Electrical and Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, Wright-Patterson AFB, Ohio.
Van Veldhuizen, D. A., & Lamont, G. B. (2000). Multiobjective evolutionary algorithms: Analyzing the state-of-the-art. Evolutionary computation, 8(2), 125-147.
Vellaiyan, S., & Amirthagadeswaran, K. S. (2016). Taguchi-Grey relational-based multi-response optimization of the water-in-diesel emulsification process. Journal of Mechanical Science and Technology, 30(3), 1399-1404.
Wang, C.-H., & Wang, J. (2014). Combining fuzzy AHP and fuzzy Kano to optimize product varieties for smart cameras: A zero-one integer programming perspective. Applied Soft Computing, 22, 410-416.
Wang, K.-C. (2011). A hybrid Kansei engineering design expert system based on grey system theory and support vector regression. Expert Systems with Applications, 38(7), 8738-8750.
Wang, L., Ng, A. H., & Deb, K. (2011). Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. London: Springer.
Wang, X. D., Hirsch, C., Kang, S., & Lacor, C. (2011). Multi-objective optimization of turbomachinery using improved NSGA-II and approximation model. Computer Methods in Applied Mechanics and Engineering, 200(9-12), 883-895.
Yadav, H. C., Jain, R., Singh, A. R., & Mishra, P. K. (2012). Robust design approach with fuzzy-AHP for product design to enhance aesthetic quality. International Journal of Design Engineering, 5(1), 65-90.
Yadav, H. C., Jain, R., Singh, A. R., & Mishra, P. K. (2017). Kano integrated robust design approach for aesthetical product design: a case study of a car profile. Journal of intelligent manufacturing, 28(7), 1709-1727.
Yang, C.-C. (2011). Constructing a hybrid Kansei engineering system based on multiple affective responses: Application to product form design. Computers & Industrial Engineering, 60(4), 760-768.
Yeh, C.-H., Deng, H., & Chang, Y.-H. (2000). Fuzzy multicriteria analysis for performance evaluation of bus companies. European Journal of Operational Research, 126(3), 459-473.
Yu, X., & Gen, M. (2010). Introduction to evolutionary algorithms. London: Springer.
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. Eidgenössische Technische Hochschule Zürich (ETH), Institut für Technische Informatik und Kommunikationsnetze (TIK).
Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3(4), 257-271.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2021-07-01起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2021-07-01起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw