- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Author Guidelines ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Table of Contents
Advances in Mechanical Engineering
Volume 2013 (2013), Article ID 234571, 13 pages
A Fuzzy Collaborative Forecasting Approach for Forecasting the Productivity of a Factory
Department of Industrial Engineering and Systems Management, Feng Chia University, 100 Wenhwa Road, Seatwen, Taichung 408, Taiwan
Received 6 February 2013; Accepted 3 July 2013
Academic Editor: Jerry Fuh
Copyright © 2013 Yi-Chi Wang and Toly Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- W. Pedrycz, “Collaborative fuzzy clustering,” Pattern Recognition Letters, vol. 23, no. 14, pp. 1675–1686, 2002.
- W. Pedrycz, “Collaborative architectures of fuzzy modeling,” Lecture Notes in Computer Science, vol. 5050, pp. 117–139, 2008.
- E. Ostrosi, L. Haxhiaj, and S. Fukuda, “Fuzzy modelling of consensus during design conflict resolution,” Research in Engineering Design, vol. 23, no. 1, pp. 53–70, 2012.
- W. J. Stevenson, Operations Management, McGraw-Hill, New York, NY, USA, 2002.
- T. Bailey, “Discretionary effort and the organization of work: employee participation and work reform since Hawthorne,” Working Paper, Columbia University, New York, NY, USA, 1993.
- T. Chen, “A flexible way of modelling the long-term cost competitiveness of a semiconductor product,” Robotics & Computer Integrated Manufacturing, vol. 29, no. 3, pp. 31–40, 2013.
- M. Godard and M. Prevost, Productivity measurements and analysis, International Congress in France Industrial Engineering and Management, 1986.
- S. Han and D. W. Halpin, “The use of simulation for productivity estimation based on multiple regression analysis,” in Proceedings of the Winter Simulation Conference, pp. 1492–1499, 2005.
- K. Huang, The application of DEA and Malmquist productivity index: the case of semiconductor industry in Taiwan [M.S. thesis], Institute of Business Administration, Chang-Gung University, Taiwan, 2005.
- M. Y. Huang and S. Y. Huang, Productivity Evaluation of Taiwanese Semiconductor Companies Using A Three-stage Malmquist DEA Approach, 2009, http://nchuae.nchu.edu.tw/tc/modules/wfdownloads/.
- L. Brandt, J. Van Biesebroeck, and Y. Zhang, “Creative accounting or creative destruction? Firm-level productivity growth in Chinese manufacturing,” Journal of Development Economics, vol. 97, no. 2, pp. 339–351, 2012.
- T. M. Wright, “Factors affecting the cost of airplanes,” Journal of Aeronautical Sciences, vol. 3, pp. 122–128, 1936.
- S. E. Black and L. M. Lynch, “How to compete: the impact of workplace practices and information technology on productivity,” Review of Economics and Statistics, vol. 83, no. 3, pp. 434–445, 2001.
- N. Bloom and J. Van Reenen, “Human resource management and productivity,” CEP Discussion Paper 982, London School of Economics and Political Science, 2010.
- P. Bandyopadhyay, J. Chowdhury, and G. Hazra, “Integration of human resource information system to DSS, CMS and other applications to increase productivity,” International Journal of Computers & Technology, vol. 3, no. 1, pp. 55–59, 2012.
- H. Tanaka and J. Watada, “Possibilistic linear systems and their application to the linear regression model,” Fuzzy Sets and Systems, vol. 27, no. 3, pp. 275–289, 1988.
- G. Peters, “Fuzzy linear regression with fuzzy intervals,” Fuzzy Sets and Systems, vol. 63, no. 1, pp. 45–55, 1994.
- S. Donoso, N. Marin, and M. A. Vila, “Quadratic programming models for fuzzy regression,” in Proceedings of International Conference on Mathematical and Statistical Modeling in Honor of Enrique Castillo, 2006.
- T. Chen and Y.-C. Wang, “A hybrid fuzzy and neural approach for forecasting the book-to-bill ratio in the semiconductor manufacturing industry,” International Journal of Advanced Manufacturing Technology, vol. 52, no. 1–4, pp. 377–389, 2011.
- O. Shai and Y. Reich, “Infused design. I. Theory,” Research in Engineering Design, vol. 15, no. 2, pp. 93–107, 2004.
- O. Shai and Y. Reich, “Infused design. II. Practice,” Research in Engineering Design, vol. 15, no. 2, pp. 108–121, 2004.
- G. Büyüközkan and Z. Vardaloǧlu, “Analyzing of collaborative planning, forecasting and replenishment approachusing fuzzy cognitive map,” in Proceedings of the International Conference on Computers and Industrial Engineering (CIE '09), pp. 1751–1756, July 2009.
- R. Poler, J. E. Hernandez, J. Mula, and F. C. Lario, “Collaborative forecasting in networked manufacturing enterprises,” Journal of Manufacturing Technology Management, vol. 19, no. 4, pp. 514–528, 2008.
- W. Pedrycz and P. Rai, “A multifaceted perspective at data analysis: a study in collaborative intelligent agents,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 38, no. 4, pp. 1062–1072, 2008.
- T. Chen, “Applying the hybrid fuzzy c-means-back propagation network approach to forecast the effective cost per die of a semiconductor product,” Computers and Industrial Engineering, vol. 61, no. 3, pp. 752–759, 2011.
- T. Chen, “Applying a fuzzy and neural approach for forecasting the foreign exchange rate,” International Journal of Fuzzy System Applications, vol. 1, no. 1, pp. 36–48, 2011.
- T. Chen, “An application of fuzzy collaborative intelligence to unit cost forecasting with partial data access by security consideration,” International Journal of Technology Intelligence and Planning, vol. 7, no. 3, pp. 201–214, 2011.
- N. Cheikhrouhou, F. Marmier, O. Ayadi, and P. Wieser, “A collaborative demand forecasting process with event-based fuzzy judgements,” Computers and Industrial Engineering, vol. 61, no. 2, pp. 409–421, 2011.
- T. Chen and Y. C. Wang, “An agent-based fuzzy collaborative intelligence approach for precise and accurate semiconductor yield forecasting,” IEEE Transactions on Fuzzy Systems. In press.
- T. Chen and Y. C. Wang, “Semiconductor yield forecasting using quadratic-programming based fuzzy collaborative intelligence approach,” Mathematical Problems in Engineering, vol. 2013, Article ID 627404, 7 pages, 2013.
- G. Büyüközkan, O. Feyzioglu, and Z. Vardaloglu, “Analyzing CPFR supporting factors with fuzzy cognitive map approach,” World Academy of Science, Engineering and Technology, vol. 31, pp. 412–417, 2009.
- R. M. Warner Jr., “Applying a composite model to the IC yield problem,” IEEE Journal of Solid-State Circuits, vol. 9, no. 3, pp. 86–95, 1974.
- Z. Zhang, “An interval-valued rough intuitionistic fuzzy set model,” International Journal of General Systems, vol. 39, no. 2, pp. 135–164, 2010.
- R. Roostaee, M. Izadikhah, F. H. Lotfi, and M. Rostamy-Malkhalifeh, “A multi-criteria intuitionistic fuzzy group decision making method for supplier selection with VIKOR method,” International Journal of Fuzzy System Applications, vol. 2, no. 1, pp. 1–17, 2012.
- G. Kabir and M. A. Hasin, “Comparative analysis of artificial neural networks and neuro-fuzzy models for multicriteria demand forecasting,” International Journal of Fuzzy System Applications, vol. 3, no. 1, pp. 1–24, 2013.