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

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.