Fuzzy clustering procedure for evaluation and selection of industrial robots

Abstract Industrial robots are increasingly used by many manufacturing firms. The number of robot manufacturers has also increased, with many now offering a wide range of robots. A potential user is faced with many options in both performance and cost. This paper proposes a decision model for the robot selection problem using fuzzy cluster analysis. Unlike most other models for robot selection, this model considers the fact that a robot's performance, as specified by the manufacturer, is often unobtainable in reality. Robots selected by the proposed model become candidates for factory testing to verify manufacturer's specifications. The proposed model is tested on a real data set, and an example is presented.

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