Bottleneck is a common term used to describe the process/operation/person that constrains the performance of the whole system. Since Goldratt introduced his theory of constraint, not many will argue about the importance of identifying and then improving the bottleneck, in order to improve the performance of the entire system. Nevertheless, there exist various definitions of bottleneck, which make bottleneck identification and improvement not a straightforward task in practice. The theory introduced by Production Systems Engineering (PSE) that the bottleneck of a production line is where the infinitesimal improvement can lead to the largest improvement of the average throughput, has provided an inspirational and rigorous way to understand the nature of bottleneck. This is because it conceptually puts bottleneck identification and improvement into a single task. Nevertheless, it is said that a procedure to evaluate how the efficiency increase of each machine would affect the total performance of a line is hardly possible in most practical situations. But is this true?

In this paper, we argue how multi-objective optimization fits nicely into the theory introduced by PSE and hence how it can be developed into a practical bottleneck improvement methodology. Numerical results from a real-world application study on a highly complex machining line are provided to justify the practical applicability of this new methodology.

Bottleneck Improvement, Production System Simulation, Multi-objective Optimization, Data Mining