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

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

Irraivan Elamvazuthi (Universiti Teknologi PETRONAS, Malaysia), Pandian Vasant (Universiti Teknologi Petronas, Malaysia) and Timothy Ganesan (Universiti Teknologi PETRONAS, Malaysia)
DOI: 10.4018/978-1-4666-1945-6.ch016
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Production control, planning, and scheduling are forms of decision making, which play a crucial role in manufacturing industries. In the current competitive environment, effective decision-making has become a necessity for survival in the marketplace. This chapter provides insight into the issues relating to integration of fuzzy logic techniques into decision support systems for profitability quantification in a manufacturing environment. The chapter is divided into five sections with a general introduction of the topic, followed by a thorough literature review on the existing techniques. Thereafter, fuzzy logic algorithms using logistic membership functions and resource variables for decision making aiming at quality improvement are discussed. A case study involving a textile firm is then described with the computational results and findings, and finally, future research directions are presented.
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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)

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