Decision Models for Robot Selection: A Comparison of Ordinary Least Squares and Linear Goal Programming Methods
- 1 March 1989
- journal article
- Published by Wiley in Decision Sciences
- Vol. 20 (1) , 40-53
- https://doi.org/10.1111/j.1540-5915.1989.tb01396.x
Abstract
The profusion of robot designs, the cost of testing, and the fact that robot operational parameter maximums are often mutually exclusive are factors that create a complex selection decision for the potential user. While formal robot testing standards are now in place, formal techniques to select robots for the testing process have not been addressed. A linear goal programming model is an effective tool for the decision maker for optimizing the robot selection process in terms of requirement priorities. It is also shown that this model provides a more stable result than the ordinary least squares estimator in the presence of statistical outliers of robot parameters. The methodology is illustrated through the use of current robot specifications.Keywords
This publication has 25 references indexed in Scilit:
- Adaptive Robust Procedures: A Partial Review and Some Suggestions for Future Applications and TheoryJournal of the American Statistical Association, 1974
- A Robust Method for Multiple Linear RegressionTechnometrics, 1974
- L p -methods for robust regressionBIT Numerical Mathematics, 1974
- Robust Estimation of Straight Line Regression Coefficients by Minimizing pth Power DeviationsTechnometrics, 1972
- Nonparametric Estimate of Regression CoefficientsThe Annals of Mathematical Statistics, 1971
- L 1 Approximation and the Analysis of DataJournal of the Royal Statistical Society Series C: Applied Statistics, 1968
- The Examination and Analysis of ResidualsTechnometrics, 1963
- A Note on Curve Fitting with Minimum Deviations by Linear ProgrammingJournal of the American Statistical Association, 1961
- Rejection of OutliersTechnometrics, 1960
- Linear Programming Techniques for Regression AnalysisJournal of the American Statistical Association, 1959