Robot selection using a fuzzy regression-based decision-making approach

Industrial robots, which enable manufacturing firms to produce high-quality products in a cost-effective manner, are important components of advanced manufacturing technologies. The performance of industrial robots is determined by multiple and conflicting criteria that have to be simultaneously con...

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Bibliographic Details
Published in:International journal of production research 2012-12, Vol.50 (23), p.6826-6834
Main Authors: Karsak, E. Ertugrul, Sener, Zeynep, Dursun, Mehtap
Format: Article
Language:English
Subjects:
Advanced manufacturing technologies
advanced manufacturing technology
Applied sciences
Automation
Computer science
control theory
systems
Control theory. Systems
Decision making models
decision support systems
Decision theory. Utility theory
Exact sciences and technology
Fuzzy
Fuzzy logic
fuzzy regression
Fuzzy set theory
Industrial robots
Information systems. Data bases
Linear inference, regression
Manufacturing engineering
Mathematics
Memory organisation. Data processing
Operational research and scientific management
Operational research. Management science
Probability and statistics
Regression
Regression analysis
robot selection
Robotics
Robots
Sciences and techniques of general use
Software
Statistics
Studies
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Summary:Industrial robots, which enable manufacturing firms to produce high-quality products in a cost-effective manner, are important components of advanced manufacturing technologies. The performance of industrial robots is determined by multiple and conflicting criteria that have to be simultaneously considered in a robust selection study. In this study, a decision model based on fuzzy linear regression is presented for industrial robot selection. Fuzzy linear regression provides an alternative approach to statistical regression for modelling situations where the relationships are vague or the data set cannot satisfy the assumptions of statistical regression. The results obtained by employing fuzzy linear regression are compared with those of earlier studies applying different analytical methods to a previously reported robot selection problem.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2011.627886