Models and algorithms for throughput improvement problem of serial production lines via downtime reduction

Abstract

Throughput is one of the key performance indicators for manufacturing systems, and its improvement remains an interesting topic in both industrial and academic fields. One way to achieve improvement is to reduce the downtime of unreliable machines. Along this direction, it is natural to pose questions about the optimal allocation of improvement effort to a set of machines and failure modes. This article develops mixed-integer linear programming models to improve system throughput by reducing downtime in the case of multi-stage serial lines. The models take samples of processing time, uptime and downtime as input, generated from random distributions or collected from real system. To improve computational efficiency while guaranteeing the exact optimality of the solution, algorithms based on Benders Decomposition and discrete-event relationships of serial lines are proposed. Numerical cases show that the solution approach can significantly improve efficiency. The proposed modeling and algorithm is applied to throughput improvement of various systems, including a long line and a multi-failure system, and also to the downtime bottleneck detection problem. Comparison with state-of-the-art approaches shows the effectiveness of the approach. Supplementary materials are available for this article. Go to the publisher’s online edition of IISE Transactions.

Keywords:

• Manufacturing systems
• serial production line
• mathematical programming
• discrete-event systems
• performance improvement

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under grant 61473188.

Notes on contributors

Mengyi Zhang

Mengyi Zhang is Ph.D. candidate in the Department of Mechanical Engineering at Politecnico di Milano, Italy. She received her B.E. degree in mechanical engineering in 2014 at Shanghai Jiao Tong University, China, the degree of ingénieur civil des mines in decision and production system engineering in 2015, at École nationale supérieure des mines de Nancy, France, and M.Sc. degree in industrial engineering in 2017, at Shanghai Jiao Tong University, China. Her research interests include simulation optimization of manufacturing systems.

Andrea Matta

Andrea Matta is a professor of manufacturing system in the Department of Mechanical Engineering at Politecnico di Milano, where he currently teaches integrated manufacturing systems and manufacturing. His research area includes analysis and design of manufacturing and health care systems. He is Editor-in-Chief of Flexible Services and Manufacturing Journal.