Double-tolerance design for manufacturing systems

Abstract

Most production environments are stochastic in nature, due to the randomness inherent in the production processes. One important engineering problem commonly faced by practitioners is to determine optimal engineering tolerances to be used in production. This article develops optimization models for determining tolerance sets to maximize the long-run average net profit on a production line with processing and rework stations, as well as instantaneous inspection and scrap operations. We assume that only one server works at the rework station, and the service times at the processing and rework stations are uncertain, thus, a stochastic queueing system is embedded into the manufacturing process. We also consider the trade-off between the overall production cost and the cost associated with a quality loss in the final product. Our work is the first to introduce the concept of double-tolerance sets to the tolerance design optimization literature. By comparing the proposed double-tolerance model with a single-tolerance model, we investigate the impact of different parameter settings and modeling assumptions on the optimal tolerances through numerical examples and a sensitivity analysis.

Keywords:

• Quality control
• double tolerance
• rework queue
• general service times
• nonlinear programming

Additional information

Notes on contributors

Di Liu

Di Liu received her BS in quality and reliability engineering from Beihang University, and her MS in industrial engineering from Clemson University. She is currently a PhD student in the Industrial Engineering Department at Clemson University, USA. Her research areas are in the fields of stochastic processes and optimization in quality engineering.

Tugce Isik

Tugce Isik received her BS in industrial engineering from Bogaziçi University, and her MS and PhD in operations research from Georgia Institute of Technology. She is currently an assistant professor in the Industrial Engineering Department at Clemson University, USA. Her research areas include operations planning and control, stochastic processes and optimization, queueing networks, and Markov decision processes, with applications to agile production and service systems.

B. Rae Cho

B. Rae Cho received his MS and PhD in industrial & systems engineering and industrial engineering from the Ohio State University and the University of Oklahoma, respectively. He is a Professor Emeritus in the Industrial Engineering Department at Clemson University, USA. His research interests are in the fields of quality engineering and process improvement, with a particular focus on deterministic and stochastic mathematical modeling in robust parameter design and statistical convolution. He served as the Editor-in-Chief of several quality-focused journals.