Development of a Structured Methodology for Knowledge Elicitation: Applications to Flexible Manufacturing Systems

Abstract

While automation in the discrete parts manufacturing industry has increased productivity and improved product quality it has also significantly altered the role played by the operator of the system. The operators' role has not only become more critical to the effective functioning of the system but also causes mental overload and imposes greater demands on humans' information processing capabilities. This paper examines methods by which the mental overload experienced by operators can be alleviated while sustaining improvements in productivity and product quality. Additionally, the possibility of reducing mental overload by training and aiding the operator and effecting a more optimal allocation of function using dynamic allocation of function schemes is reviewed. It is concluded that a prerequisite to using the above methods to reduce mental overload is the ability to build appropriate decision support systems. A methodology that allows the development of such decision support systems is described and results of a study to evaluate the methodology are presented.

Notes

Handled by the Department of Computer and Information Sciences