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: 10.4018/978-1-4666-0294-6.ch007
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Abstract

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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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)

Key Terms in this Chapter

Logistic Membership Function: An appropriate function to represent the degree of truth

Quality Improvement: Systematic approach to improve product / service level compared with accepted standards

Fuzzy Set Theory: Set membership that handles the concept of partial truth where the truth values fall between completely true and completely false

Optimal Value: Best state that a value could achieve

Objective Function: A function that can be made as large or as small as possible

Manufacturing Environment: An industrial operational facility to fabricate products

Resource Variable: A resource is used to represent dynamic data that holds as a reference to an external resource

Decision Support System: A method to model data and make quality decisions based on it to support business or organizational decision-making activities

Fuzzy Linear Programming: An application of fuzzy set theory in linear decision making problems to determine an optimal solution by considering a number of constraints

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