GOALI: Planning Horizons for Optimal Decision Making Over Time with Applications to Production Systems Optimization

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

Within the context of today's rapidly changing world, firms are confronted daily with the challenge of making decisions that not only make economic sense now but also position the firm for grappling with a future often characterized by rapidly evolving technology and markets. Key to making these decisions are the planning horizons employed. Historically, selection of planning horizons has been based upon tradition or engineering judgment. If that horizon is myopic, the resulting decision can be short-sighted and fail to anticipate events that can render today's decision unwise. If the horizon is too long, considerable resources may be expended to collect irrelevant data and the resulting problem can present formidable computational demands. The overall goal of this research is to provide a rational framework that leads to a planning horizon choice that is efficient and yet far-sighted, leading to decisions which are undistorted by unanticipated end-of-study effects. Examples of such decision-making problems include the sizing and timing of capacity expansions, planning for production scheduling and maintenance tasking, and the replacement and acquisition of new equipment. We propose to validate the models and methods developed on the problem of jointly optimizing manufacture and maintenance schedules in the context of vehicle production along a collection of production lines at General Motors. The research will be collaboratively pursued with faculty and students at the University of Michigan and research engineering staff at General Motors R & D Laboratories. The intellectual merit of this work includes establishing methods and conditions under which one may finitely compute an optimal first policy to a problem with infinite data, thereby extending solution procedures to cover nonhomogeneous Markov decision processes. The broad impact of this work lies in the potential of the models, algorithms, and rules-of-thumb developed to assist decision makers in deciding how far into the future they need to look to make a wise decision today. This research will be grounded in a real application arena at General Motors with the intent of increasing the productivity and reliability of national manufacturing systems. Students will serve as interns at GM thus directly assisting in the transfer of technology both in the research and educational domains.