Abstract
The yield of the wafer slicing process has the greatest impact on manufacturing costs in the fabrication of photovoltaic (PV) cells. Hence, it is critical to identify the correct type of wire saw for this process. This paper employs the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS) to construct a collaborative decision model for predicting the yield of a wire saw. The evaluation criteria for establishing the model are derived on the basis of a literature review and the opinions of experts with experience in PV wafer manufacturing. The evaluation weights are determined by the AHP and the optimal machine is identified by the TOPSIS. Finally, process capability indices are presented to demonstrate and verify the feasibility and effectiveness of the proposed method.
Similar content being viewed by others
References
Albayrak Y. E., Erensal Y. C. (2009) Leveraging technological knowledge transfer by using fuzzy linear programming technique for multiattribute group decision making with fuzzy decision variables. Journal of Intelligent Manufacturing 20(2): 223–231
Buyukozkan G., Feyzioglu O., Nebol E. (2008) Selection of the strategic alliance partner in logistics value chain. International Journal of Production Production Economics 113(1): 148–158
Chang C. W., Wu C. R., Lin C. T., Chen H. C. (2007) An application of AHP and sensitivity analysis for selecting the best slicing machine. Computers and Industrial Engineering 55(2): 296–307
Chang, C. W., Horng, D. J., & Lin, H. L. (2009). An IS quality measurement using gap and MCDM: A case study for supply chain management systems. Journal of Testing and Evaluation, 37(2), Paper ID JTE102004.
Chiu C. Y., Chou W. L., Homg S. M., Kuo R. J. (2003) A Wafer fabrication dispatching method for minimizing inventory variability using fuzzy inference. International Journal of Industrial Engineering 10(4): 621–627
Chen, W. C., & Chien, C. F. (2009). Measuring relative performance of wafer fabrication operations: A case study. Journal of Intelligent Manufacturing, Online First.
Clements J. A. (1989) Process capability calculations for non-normal distributions. Quality Progress 22(9): 95–100
Ertugrul I., Karakasoglu N. (2008) Comparison of fuzzy AHP and fuzzy TOPSIS methods for facility location selection. International Journal of Advanced Manufacturing Technology 39(7–8): 783–795
Hwang C. L., Yoon K. P. (1981) Multiple attribute decision making: Methods and applications, a state-of-the-art survey. Springer, New York
Kuo Y., Yang T., Cho C., Tseng Y. C. (2008) Using simulation and multi-criteria methods to provide robust solutions to dispatching problems in a flow shop with multiple processors. Mathematics and Computers in Simulation 78(1): 40–56
Lin M. C., Wang C. C., Chen M. S., Chang C. A. (2008) Using AHP and TOPSIS approaches in customer-driven product design process. Computers in Industry 59(1): 17–31
Lin C. T., Chang C. W., Chen C. B. (2005) Relative control philosophy- balance and continual change for forecasting abnormal quality characteristics in a silicon Wafer slicing process. International Journal of Advanced Manufacturing Technology 26(9–10): 1109–1114
Lin C. T., Chang C. W., Chen C. B. (2006) The worst Ill-condition of silicon Wafer slicing machine detecting by using grey relational analysis. International Journal of Advanced Manufacturing Technology 31(3–4): 388–395
Lin C. T., Chen C. B., Chang C. W. (2002) Screening synchronously occurred multiple abnormal quality characteristics screening in a silicon Wafer slicing process. The Asian Journal on Quality 3(1): 48–60
Metin D. (2008) Decision making in equipment selection: an integrated approach with AHP and PROMETHEE. Journal of Intelligent Manufacturing 19(4): 397–406
Norita A., Robin G. Q. (2009) Integrated model of operations effectiveness of small to medium-sized manufacturing enterprises. Journal of Intelligent Manufacturing 20(1): 79–89
Onut S., Soner S. (2008) Transshipment site selection using the AHP and TOPSIS approaches under fuzzy environment. Waste Management 28(9): 1552–1559
Oenuet S., Kara S. S., Efendigil T. (2008) A hybrid fuzzy MCDM approach to machine tool selection. Journal of Intelligent Manufacturing 19(4): 443–453
Pai P. F., Lee C. E., Su T. H. (2004) A daily production model for Wafer fabrication. International Journal of Advanced Manufacturing Technology 23(1): 58–63
Rao R. V. (2008) Evaluation of environmentally conscious manufacturing programs using multiple attribute decision-making methods. Proceedings of the Institution of Mechanical Engineers Pare B-Journal of Engineering Manufacture 222(3): 441–451
Saaty T. L. (1990) How to mark a decision: The analytic hierarchy process. European Journal of Operational Research 48(1): 9–26
Saaty T. L. (1980) The analytic hierarchy process. McGraw Hill, New York
Semih Ö., Selin S. K., Tuǧba E. (2008) A hybrid fuzzy MCDM approach to machine tool selection. Journal of Intelligent Manufacturing 19(4): 443–453
Wang K., Wang C. K., Hu C. (2005) Analytic hierarchy process with fuzzy scoring in evaluating multidisciplinary R & D projects in China. IEEE Transactions on Engineering Management 52(1): 119–129
Wu H. H., James J. S. (2001) A Monet Carlo comparison of capability indices when processes are non-normally distributed. Quality and Reliability Engineering International 17: 219–231
Yurdakul M., IC Y. T. (2005) Development of a performance measurement model for manufacturing companies using the AHP and TOPSIS approaches. International Journal of Production Research 43(21): 4609–4641
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chang, CW. Collaborative decision making algorithm for selection of optimal wire saw in photovoltaic wafer manufacture. J Intell Manuf 23, 533–539 (2012). https://doi.org/10.1007/s10845-010-0391-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-010-0391-6