The time interval alignment (IA) policies, boundary and applications with multiple stream arrivals

Meimei Wang , James R. Perkins

Journal of Systems Science and Systems Engineering ›› 2011, Vol. 20 ›› Issue (4) : 400 -415.

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Journal of Systems Science and Systems Engineering ›› 2011, Vol. 20 ›› Issue (4) : 400 -415. DOI: 10.1007/s11518-011-5183-9
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The time interval alignment (IA) policies, boundary and applications with multiple stream arrivals

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Abstract

This paper discusses a class of interval alignment (IA) scheduling policies, which are particularly effective for the systems that do not have Markovian structure. The numerical results show that IA policies effectively smooth part flows, improve performance and decrease average Work-in-Process (WIP) by adding intermediate delays to the system. The boundary of IA policy is proven and the applications of IA policy in the system with multiple stream arrivals have been discussed. With the combination of release policy, it is practical to implement IA to multiple stream arrival system.

Keywords

Detailed scheduling policies / non-Markovian / multiple stream / interval alignment policies / integer linear programming / lean systems

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Meimei Wang, James R. Perkins. The time interval alignment (IA) policies, boundary and applications with multiple stream arrivals. Journal of Systems Science and Systems Engineering, 2011, 20(4): 400-415 DOI:10.1007/s11518-011-5183-9

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References

[1]

Gershwin S.B.. A Hierarchical Framework for Discrete-Event Scheduling. Control and Information Science, 1987, New York, NY: Springer-Verlag

[2]

Glasserman P.. Hedging-point production control with multiple failure modes. IEEE Transactions on Automatic Control, 1995, 40(4): 707-712.

[3]

Glassey C.R., Resende M.. Closed-loop job release control for VLSI circuit manufacturing. IEEE Transactions on Semiconductor Manufacturing, 1988, 1: 36-46.

[4]

Harrison J.M., Wein L.M.. Scheduling networks of queues: heavy traffic analysis of a two-station closed network. Operations Research, 1990, 38(6): 1052-1064.

[5]

Hodgson T., Deleeranyder J., King R.. Intergration Kanban types pull systems and MRP type push systems: insight from a Markovian model. IIE Transactions, 1992, 24(3): 43-56.

[6]

Jain S., Johnson M., Safai F.. Implementing setup optimization on the shop floor. Operations Research, 1996, 44(6): 843-851.

[7]

Kleinrock L.. Queueing Systems, Volume 1: Theory, 1975, New York, NY: John Wiley and Sons

[8]

Kogan K., Leu Y.Y., Perkins J.R.. Parallel-machine, multiple-product-type, continuous-time scheduling: decomposable cases. IIE Transactions, 2002, 34(1): 11-22.

[9]

Lambrecht M.R., Ivens P.L., Vandaele N.J.. ACLIPS: a capacity and lead time integrated procedure for scheduling. Management Science, 1998, 44(11): 1548-1561.

[10]

Liberopoulos G., Caramanis M.. Production control of manufacturing systems with production ratedependent failure rates. IEEE Transactions on Automatic Control, 1994, 39(4): 889-895.

[11]

Lu S.C.H., Ramaswamy D., Kumar P.R.. Efficient scheduling policies to reduce mean and variance of cycle-time in semiconductor manufacturing plants. IEEE Transactions on Semiconductor Manufacturing, 1994, 7(3): 374-385.

[12]

Meal H.C., Wachter M.H., Whybark D.C.. Material requirement planning in hierarchical production planning systems. International Journal of Production Research, 1987, 25(7): 947-956.

[13]

Perkins, J.R. (1995). Optimal control of manufacturing systems with buffer holding costs. In: 3rd SIAM Conference on Control and Its Application, St Louis, MO, April 1995

[14]

Perkins J.R., Srikant R.. Hedging policies for failure-prone manufacturing systems: optimality of JIT policies and bounds on buffer levels. IEEE Transactions on Automatic Control, 1998, 43: 953-957.

[15]

Perkins J.R., Humes Jr.C., Kumar P.R.. Distributed scheduling of flexible manufacturing systems: stability and performance. IEEE Transactions on Robotics and Automation: Special section on computer integrated manufacturing, 1994, 10(2): 133-141.

[16]

Rishel R.. Dynamic programming and minimum principles for systems with jump Markov disturbances. SIAM Journal of Control, 1975, 13(2): 338-371.

[17]

Seidman T.I.. ’First Come, First Served’ can be unstable!. IEEE Transactions on Automatic Control, 1994, 39: 2166-2171.

[18]

Shu C., Perkins J. R.. Optimal PHP production of multiple part-types on a failure-prone machine with quadratic buffer costs. IEEE Transactions on Automatic Control, 2001, AC-46: 541-549.

[19]

Sipper D., Bulfin R.L.. Production: Planning, Control, and Integration, 1997, New York, NY: McGraw-Hill Company Inc.

[20]

Suri R.. Quick Response Manufacturing: A Company Wide Approach to Reducing Lead Times, 1998, Portland, Oregon: Productivity Press

[21]

Wang M., Perkins J.R.. Using interval alignment policies for efficient production control of supply chain systems. International Journal of Industrial and System Engineering, 2006, 1(1): 87-108.

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