- E-ISSN2233-5382
- KCI
E-ISSN : 2233-5382
Fuzzy and Multi Criteria Decisions for Business Management in Product Design Industries
Abstract
Purpose - This study illustrates research product industrial engineering, which needs to be promoted to encourage knowledge intensive businesses. Research traditions related to industrial business products and a fuzzy multi criteria decision approach in technology management for product design industries have undergone continuous changes over time. However, there is no clarity on the present situation, and there is a need to reform business enterprises. Research design, data, and methodology - Using fuzzy theory and appraising multi-goal plans, the manner of promoting the competitive advantage of industrial businesses is analyzed using a case study. In the case study, various aspects are examined, such as product design and manufacture, fuzzy set decisions with multi attribute policy making, flaws in the present system, and a review of the related literature. Results - New fuzzy and multi criteria designs can improve the existing keyboard by solving product problems, resulting in a clear and durable typeface for a creative LED keyboard. Conclusion - Using a fuzzy set with multi attribute policy-making influences the achievements appraisal system and can help achieve the anticipated strategy goal of product design.
- keywords
- Industrial Business, Fuzzy Theory, Tradition Product Design, A Fuzzy and Multi Criteria Decision, Product Design
Reference
Aburas, H.M. (2010). An integrated performance management framework for a multi-business company. South African Journal of Industrial Engineering, 1(21), 35-43.
Botha, G.J., van ＆ Rensburg, A.C. (2010). Proposed business process improvement model with integrated customer experience management. South African Journal of Industrial Engineering, 1(21), 45-57.
Chiou, H.K., ＆ Tzeng, G.H. (2002). Fuzzy Multiple-Criteria Decision-Making Approach for Product Design Decision Flow. New York : Springer-Veriag New York Inc.
Erasmus, P., ＆ van Waveren, C.C. (2009). Evaluation of quality concepts influencing a manufacturing environment in SOUTH AFRICA. South African Journal of Industrial Engineering November, 2(20), 93-105.
Illier, S. F., ＆ Lieberman, J. G. (1996). Introduction to Operations Research, Mc Graw Hill, lein M. Cerry. Fuzzy Sets and Systems, 1(39), 27-41.
Javid, A. A., ＆ Davoudpour, H. (2009). A new model for single facility location based on service level. South African Journal of Industrial Engineering, 2(20), 219-227.
Lotan, T., ＆ Koutsopoulos, H.N.(1992). Route choice in the presence of information using concepts from Fuzzy control and approximate reasoning. Transportation Planning and Technology, 2(17), 113-126.
Min, H. (1991). A multi-objective vehicle routing problem with soft time windows: the case of a public library distribution system. Socio-Economic Planning Science, 3(25), 179-188.
Mote, J., Murthy, O., ＆ Olson, D. L. (1991). A parametric approach to solving bicriterion shortest path problems. European Journal of Operational Research, 1(53), 81-92.
Salo, A.A. (1994). On Fuzzy ratio comparisons in hierarchical decision models. Fuzzy Sets and Systems, 1(84), 21-32.
Singh, N., ＆ Mohanty, B. K. (1991). A Fuzzy approach to multi-objective routing problem with application to process planning in manufacturing systems. International Journal of Production Research, 39(6), 1161-1170.
Tzeng, G.H., Yang, Y.P., Lin, C.T., ＆Chen, C. B. (2004). Hierarchical MADM with Fuzzy intergral for evaluating enterprise intranet web sites. Information Sciences, 169(3-4), 409-426.
Tzeng, G.H., Chiang, C.H., ＆Li, C.W. (2006). Evaluating intertwined effects in e-leaning programs: A novel hybrid MCDM model based on factor analysis and dematel. Expert System with Applications, 4(32), 115-119.
Teng, J.Y., ＆ Tzeng, G.H. (1996). Fuzzy multicriteria ranking of urban transportation investment alternatives. Transportation Planning and Technology, 1(20), 15-31.
Teodorovic, D. (1994). Fuzzy sets theory applications in traffic and transportation. European Journal of Operational Research, 3(74), 379-390.
Teodorovic, D., ＆ Kikuchi, S. (1991). Application of Fuzzy set theory to the savings based vehicle routing algorithm. Civil Engineering Systems, 2(8), 87-93.
Uys, J.W., Schutte, C.S.L., ＆ Esterhuizen, D. (2010). Trends in a South African industial engineering research journal: A textual information analysis perspective. South African Journal of Industrial Engineering, 1(21), 1-16.
Van Landeghem, H. E. (1988). A bi-criteria heuristic for the vehicle routing problem with time window. European Journal of Operational Research, 2(36), 217-226.
Van Laarhoven, P.J., ＆ Pedrycz, W. (1983). A Fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 1(11), 229-241.
William, K. K., & Michael, J. S. (2002).Psychophysical assessments of image-sensor fused imagery. Publish Information, 44(2), 257-265.
Wu, C.H., Tzeng, G.H., Goo, Y.J., & Fang, W.C. (2007). A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert Ssystems with Applications, 32(2), 398-408.
- Downloaded
- Viewed
- 0KCI Citations
- 0WOS Citations