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A Decision Model for the Robot Selection Problem Using Robust Regression *
Author(s) -
Khouja Moutaz,
Booth David E.
Publication year - 1991
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1991.tb01288.x
Subject(s) - robot , computer science , selection (genetic algorithm) , set (abstract data type) , model selection , decision model , key (lock) , regression analysis , linear regression , range (aeronautics) , artificial intelligence , machine learning , engineering , computer security , programming language , aerospace engineering
Industrial robots are increasingly used by many manufacturing firms. The number of robot manufacturers has also increased with many of these firms now offering a wide range of models. A potential user is thus faced with many options in both performance and cost. This paper proposes a decision model for the robot selection problem. The proposed model uses robust regression to identify, based on manufacturers' specifications, the robots that are the better performers for a given cost. Robust regression is used because it identifies and is resistant to the effects of outlying observations, key components in the proposed model. The robots selected by the model become candidates for testing to verify manufacturers' specifications. The model is tested on a real data set and an example is presented.