Simulation test bed for manufacturing analysis: comparison of bottleneck detection methods for AGV systems

ABSTRACT

The performance of a manufacturing or logistic system is determined by its constraints. Therefore, in order to improve the performance, it is necessary to improve the constraints, also known as the bottlenecks. Finding the bottlenecks, however, is not easy. This paper compares the two most common bottleneck detection methods, based on the utilization and the waiting time, with the shifting bottleneck detection method developed by us, for AGV systems. We find that the two conventional methods have many shortcomings compared to the shifting bottleneck detection method. In the example presented here, conventional methods are either unable to detect the bottleneck at all or detect the bottleneck incorrectly. The shifting bottleneck detection method not only finds the bottlenecks but also determines the magnitude of the primary and secondary bottlenecks.

REFERENCES

• 1: Blackstone, J. H. 2001. Theory of constraints - a status report. International Journal of Production Research, 39(6): 1053--1080.
• 2: Bukchin, J. 1998. A comparative study of performance measures for throughput of a mixed model assembly line in a JIT environment. International Journal of Production Research, 36(10): 2669--2685.
• 3: Chiang, S. -Y., Kuo, C. -T., and Meerkov, S. M. 1998. Bottlenecks in Markovian Production Lines: A Systems Approach. IEEE Transactions on Robotics and Automation, 14(2): 352--359.
• 4: Chiang, S. -Y., Kuo, C. -T., and Meerkov, S. M. 2000. DT-Bottlenecks in Serial Production Lines: Theory and Application. IEEE Transactions on Robotics and Automation, 16(5): 567--580.
• 5: Chiang, S. -Y., Kuo, C. -T., and Meerkov, S. M. 2002. c-Bottlenecks in Serial Production Lines: Identification and Application. Mathematical Problems in Engineering, to appear 2002.
• 6: Delp, D., Si, J., Hwang, Y., and Pei, B. 2003. A Dynamic System Regulation Measure for Increasing Effective Capacity: the X-Factor Theory. In IEEE/SEMI Advanced Semiconductor Manufacturing Conference and Workshop (ASMC), ed., Munich, Germany.
• 7: Goldratt, E. M. 1992. The Goal: A Process of Ongoing Improvement. North River Press.
• 8: Kuo, C. -T., Lim, J. -T., and Meerkov, S. M. 1996. Bottlenecks in Serial Production Lines: A System-Theoretic Approach. Mathematical Problems in Engineering, 2: 233--276.
• 9: Lawrence, S. R., and Buss, A. H. 1995. Economic Analysis of Production Bottlenecks. Mathematical Problems in Engineering, 1(4): 341--369.
• 10: Li, J., and Meerkov, S. M. 2000. Bottlenecks with Respect to Due-Time Performance in Pull Serial Production Lines. Mathematical Problems in Engineering, 5: 479--498.
• 11: Luthi, J. 1998. Interval Matrices for the Bottleneck Analysis of Queueing Network Models with Histogram-Based Parameters. In IEEE International Computer Performance & Dependability Symposium, 142--151, Durham, NC, USA: IEEE Computer Society Press.
• 12: Luthi, J., and Haring, G. 1997. Bottleneck Analysis for Computer and Communication Systems with Workload Variabilities & Uncertainties. In Proceedings of 2nd International Symposium on Mathematical Modelling, ed. I. Troch and F. Breitenecker, 525--534, Vienna, Austria.
• 13: Nakano, M., Sugiura, N., Tanaka, M., and Kuno, T. 1994. ROPSII: Agent Oriented Manufacturing Simulator on the basis of Robot Simulator. In Japan-USA Symposium on Flexible Automation, 201--208, Kobe, Japan.
• 14: Christoph Roser , Masaru Nakano , Minoru Tanaka, A practical bottleneck detection method, Proceedings of the 33nd conference on Winter simulation, December 09-12, 2001, Arlington, Virginia
• 15: Christoph Roser , Masaru Nakano , Minoru Tanaka, Productivity improvement: shifting bottleneck detection, Proceedings of the 34th conference on Winter simulation: exploring new frontiers, December 08-11, 2002, San Diego, California
• 16: Roser, C., Nakano, M., and Tanaka, M. 2002b. Tracking Shifting Bottlenecks. In Japan-USA Symposium on Flexible Automation, 745--750, Hiroshima, Japan.