Bottleneck detection in high-variety make-to-Order shops with complex routings: an assessment by simulation
通过仿真评估了多种方法在高品种按订单生产车间中检测瞬时瓶颈的效果，发现基于设备利用率的检测方法表现最佳但波动大，而基于活跃期的方法综合表现更好。
This study uses simulation to assess the performance of alternative methods for detecting momentary bottlenecks in high-variety contexts that produce on a to-order basis. The results suggest that using the utilisation level of a station to detect bottlenecks leads to the best performance, but that this method suffers from high nervousness. Using the active period of a station appears to be a better overall choice for practice given its good performance and low nervousness. Meanwhile, methods that focus on the workload at a station are a viable alternative, but they may become dysfunctional in shops with directed routings and a limit on the queue. This negative effect is even stronger if the corrected workload measure is used, as recently suggested in the literature on short term capacity adjustments. Finally, using the inter-departure time detection method leads to the worst performance since: (i) it counterintuitively detects non-bottlenecks instead of bottlenecks; and, (ii) it is based on historical data, leading to a response delay.