An integrated model for robot selection in robotic cells under uncertain situations

Abstract

Robots have become an extremely important part of society today; therefore, optimal selection of a robot is more important than ever. Companies, decision-makers and experts have experimented with several methodologies for selecting the proper robot to meet their needs and make an overall good business decision. This paper applies fuzzy AHP with QFD to compensate for the vagueness and imprecision of the data to provide the best ranking and customer needs for the weights given for the options to make an optimal selection for a robot. The case study discusses how this is an important aspect of a production environment and how it can provide great benefits with proper implementation.