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A Robust Multivariate Statistical Procedure for Evaluation and Selection of Industrial Robots

David E. Booth (Kent State University, Ohio, USA)
Moutaz Khouja (The University of North Carolina, Charlotte, North Carolina)
Michael Hu (Kent State University, Ohio, USA)

International Journal of Operations & Production Management

ISSN: 0144-3577

Article publication date: 1 February 1992

Abstract

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 robots. A potential user is thus faced with many options in both performance and cost. Proposes a decision model for the robot selection problem using both a robustified Mahalanobis distance analysis, i.e. a multivariate distance measure, and principal‐components analysis. Unlike most other models for robot selection, this model takes into consideration the fact that a robot′s performance, as specified by the manufacturer, is often unobtainable in reality. The robots selected by the proposed model become candidates for factory testing to verify manufacturers′ specifications. Tests the proposed model on a real data set and presents an example.

Keywords

Citation

Booth, D.E., Khouja, M. and Hu, M. (1992), "A Robust Multivariate Statistical Procedure for Evaluation and Selection of Industrial Robots", International Journal of Operations & Production Management, Vol. 12 No. 2, pp. 15-24. https://doi.org/10.1108/01443579210009023

Publisher

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MCB UP Ltd

Copyright © 1992, MCB UP Limited