Dynamic control in automated manufacturing: a knowledge integrated approach†

This paper first presented at the Symposium on Real Time Optimization in Automated Manufacturing Facilities; National Bureau of Standards; Gaithersburg, MD, U.S.A., 21–22 January, 1986.

Abstract

Flexibly automated facilities permit a wider variety of products as well as objectives for making those products—thus requiring manufacturing control strategies to face an environment of ever present change. To operate in this environment, a system composed of hard automation, flexible automation and humans, which can be responsive to product and process requirements, machine breakdowns and delays, engineering changes and improvement opportunities, is needed. Such a system does not fall into the realm of any current manufacturing solution techniques. Something more than exact optimization, heuristic algorithms or stochastic estimates must be utilized. The research discussed herein describes a dynamic solution strategy to operate in this changing environment with adaptive self-improving characteristics. The proposed methodology for optimizing the control of an automated manufacturing facility is an integrated approach utilizing real-time feedback from the operating facility, direct feedback from a simulation of the facility and guidance from a historical knowledge base. This system is being implemented in a knowledge based environment called CAYENE. CAYENE is a hybrid artificial intelligence system, written in Lisp, based on the idea of using object oriented programming as a unifying principle for functional, frame and rule-based programming.

Notes

This paper first presented at the Symposium on Real Time Optimization in Automated Manufacturing Facilities; National Bureau of Standards; Gaithersburg, MD, U.S.A., 21–22 January, 1986.