Estimating flowtimes and setting due-dates in complex production systems

Abstract

This paper presents a methodology for estimating flowtimes and setting due-dates in complex production systems. This is accomplished by modeling flowtime estimation as a forecasting problem, and using the empirical distribution of forecast errors to set job due-dates in production settings with multiple workcenters, multiple servers, feedback queues, and machine breakdowns. Several due-date performance objectives are considered, including cost minimization, attainment of service level targets, and minimization of mean absolute lateness and mean squared lateness. Simulation experiments demonstrate the effectiveness of the method in comparison with both theoretical and empirical methods previously introduced in the literature.

Additional information

Notes on contributors

STEPHEN R. LAWRENCE

Stephen R. Lawrence is an Assistant Professor of Operations Management in the College of Business of the University of Colorado at Boulder. His current research interests include economic scheduling, international and environmental operations, capacity design and planning, and operations strategy. He teaches operations strategy, technology management, and environmental operations to undergraduates, MBAs, and executives. Previously, he worked in industry in operations management, quality management, and sales engineering.