Statistical procedures for task assignment and robot selection in assemblycells

The put-pose of this paper is to show how statistical procedures can be use d to design robotic assembly cells. The proposed methodology has two stages . In the first stage, a fuzzy clustering algorithm is employed to group sim ilar tasks together so that they can be assigned to robots while maintainin g a balanced cell and achieving a desired production cycle time. In the sec ond stage, a Mahalanobis distance procedure is used to select robots approp riate for the task groups. The proposed approach recognizes and exploits th e flexibility of robots. It also recognizes that the manufacturer specifica tions of robots do not hold simultaneously under normal operating condition s. A numerical example is presented and a small experiment is conducted to test die procedures.