Decision Models for Robot Selection: A Comparison of Ordinary Least Squares and Linear Goal Programming Methods
比较了普通最小二乘法和线性目标规划在机器人选择中的效果，发现线性目标规划在存在统计异常值时更稳定，能根据需求优先级优化选择。
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.