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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, Pandian Vasant, Timothy Ganesan
Copyright: © 2013 |Pages: 20
ISBN13: 9781466619456|ISBN10: 1466619457|EISBN13: 9781466619463
DOI: 10.4018/978-1-4666-1945-6.ch016
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MLA

Elamvazuthi, Irraivan, et al. "Integration of Fuzzy Logic Techniques into DSS for Profitability Quantification in a Manufacturing Environment." Industrial Engineering: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2013, pp. 242-261. https://doi.org/10.4018/978-1-4666-1945-6.ch016

APA

Elamvazuthi, I., Vasant, P., & Ganesan, T. (2013). Integration of Fuzzy Logic Techniques into DSS for Profitability Quantification in a Manufacturing Environment. In I. Management Association (Ed.), Industrial Engineering: Concepts, Methodologies, Tools, and Applications (pp. 242-261). IGI Global. https://doi.org/10.4018/978-1-4666-1945-6.ch016

Chicago

Elamvazuthi, Irraivan, Pandian Vasant, and Timothy Ganesan. "Integration of Fuzzy Logic Techniques into DSS for Profitability Quantification in a Manufacturing Environment." In Industrial Engineering: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 242-261. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-1945-6.ch016

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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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