Scheduling Manufacturing Systems of Re-Entrant Lines
Re-entrant lines axe manufacturing systems where parts may return more than once to the same machine, for repeated stages of processing. Examples of such systems are semiconductor manufacturing plants. We consider the problems of scheduling such systems to reduce manufacturing lead times, variations in the manufacturing lead times, or holding costs.
We assume a deterministic model, which allows for bursty arrivals. We show how one may design scheduling policies to help in meeting these objectives. To reduce the mean or variance of manufacturing lead time, we design a class of scheduling policies called Fluctuation Smoothing policies. To reduce the holding costs in systems with set-up times, we introduce the class of Clear-A-Praction scheduling policies.
We study the stability and performance of these scheduling policies. We illustrate how scheduling policies can be unstable in that the levels of the buffers become unbounded. However, we show that all Least Slack policies, including the well known Earliest Due Date policy and all the Fluctuation Smoothing policies, are stable.
KeywordsTransportation Dispatch Glean Rote
Unable to display preview. Download preview PDF.
- Steve C. H. Lu, Deepa Ramaswamy, and P. R. Kumar. Efficient scheduling policies to reduce mean and variance of cycle-time in semiconductor manufacturing plants. Technical report, University of Illinois, Urbana, IL, 1992.Google Scholar
- Sheldon M. Ross. Stochastic Processes. John Wiley, New York, 1982.Google Scholar
- Steve H. Lu and P. R. Kumar. Fluctuation smoothing scheduling policies for queueing systems. To appear in Proceedings of the Silver Jubilee Workshop on Computing and Intelligent Systems, Bangalore, India, December 1993.Google Scholar
- S. Li. Private Communication. 1992.Google Scholar
- S. Li. An introduction to basic principles of production. In Semiconductor Manufacturing Technology Workshop, pages 3–20, Hsinchu, Taiwan, R.O.C., March 1993.Google Scholar
- P. R. Kumar and Sean Meyn. Stability of queueing networks and scheduling policies. Technical report, C. S. L., University of Illinois, 1993.Google Scholar
- P. R. Kumar and Sean Meyn. Duality and linear programs for stability and performance analysis of queueing networks and scheduling policies. Technical report, C. S. L., University of Illinois, 1993.Google Scholar
- A. N. Rybko and A. L. Stolyar. On the ergodicity of stochastic processes describing open queueing networks. Problemy Peredachi Infor-matsii, 28:2–26, 1991.Google Scholar
- J. G. Dai. On the positive Harris recurrence for multiclass queueing networks: A unified approach via fluid models. Technical report, Georgia Institute of Technology, 1993.Google Scholar
- Hong Chen. Fluid approximations and stability of multiclass queueing networks I: Work conserving disciplines. Technical report, University of British Columbia, 1993.Google Scholar
- D. Bertsimas, I. Ch. Paschalidis and J. N. Tsitsiklis. Scheduling of multiclass queueing networks: Bounds on achievable performance. In Workshop on Hierarchical Control for Real-Time Scheduling of Manufacturing Systems, Lincoln, New Hampshire, October 16–18, 1992.Google Scholar
- D. Bertsimas, I. Ch. Paschalidis and J. N. Tsitsiklis. Optimization of multiclass queueing networks: Polyhedral and nonlinear characterizations of achievable performance. Laboratory for Information and Decision Systems and Operations Research Center, M. I. T., December 1992.Google Scholar
- S. Kumar and P. R. Kumar. Performance bounds for queueing networks and scheduling policies. Technical report, Coordinated Science Laboratory, University of Illinois, Urbana, IL, 1992. To appear in IEEE Transactions on Automatic Control, August 1994.Google Scholar
- C. J. Chase and P. J. Ramadge. On the real time control of flexible manufacturing systems. In Proc. IEEE 28th Conf. on Decision and Control, pages 2026–2027, Tampa, FL, 1989.Google Scholar
- J. R. Perkins, C. Humes, Jr., and P. R. Kumar. Distributed control of flexible manufacturing systems: Stability and performance. Technical report, University of Illinois, Urbana, IL, 1993. To appear in IEEE Transactions on Robotics and Automation, 1994.Google Scholar