Decision-making and performance measurement models with applications to robot selection

Many advanced manufactures use robots extensively to perform repetitious, difficult, and hazardous tasks with precision. Robots improve quality and productivity dramatically if deployed properly. The process of selecting the most suitable robot among many alternatives involves robots' performance in a number of key areas. Various quantitative methods have been proposed as an aid to selection decision on the choice of robots. This paper demonstrates the use of and compares some of the current multi-attribute decision-making (MADM) and performance measurement methods through a robots selection problem borrowed from Khouja. Particular emphasis is placed on a performance measurement procedure called operational competitiveness rating (OCRA) and an MADM tool called technique for order preference by similarity to ideal solution (TOPSIS). A rank-correlation test shows that the methods considered produce similar rankings for the robots. The final selection is made on the basis of the rankings obtained by averaging the results of OCRA, TOPSIS, and a utility model.