Integration of Fuzzy Logic Techniques into DSS for Profitability Quantification in a Manufacturing Environment

Background

The mathematical formalization of fuzziness was originally pioneered by (Zadeh, 1965) . This paved the way for more research in the application of fuzzy in real-world problems (Orlovsky, 1980). Some earlier work on fuzzy decision making can be found in (Tamiz, 1996, Zimmermann, 1987, Kickert, 1978, Zimmermann, 1991). In the last decade, manufacturing companies decided to adopt intelligent solutions, since the traditional manufacturing decision-making mechanisms were found to be insufficiently flexible to respond to changing production styles and highly dynamic variations in product requirements (Kusiak, 1990 and Metaxiotis et al, 2002). (Custodio et al, 1994) discussed the issue of production planning and scheduling using a fuzzy decision system, whilst, several outlines concerning the development of a rule-base for the specification of manufacturing planning and control systems were made by (Howard et al, 2000) . (Watada, 1997) has proposed one form of logistic membership function to overcome difficulties in using linear membership function in solving fuzzy decision making problem. Non linear logistic membership function was presented by (Vasant & Bhattacharya, 2007 and Bhattacharya & Vasant, 2007). Some representative publications can also be found in (Zimmermman, 1985, Yager et al, 1987, Dubois & Prade, 1980Klir, & Yuan, 1995 and Ross, 1995)