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Stochastic Flow-Shop Scheduling with Lateness-Related Performance Measures

Published online by Cambridge University Press:  27 July 2009

Chung-Yee Lee  and
Chen-Sin Lin
Chung-Yee Lee
Affiliation:
Industrial and Systems Engineering DepartmentThe University of Florida Gainesville, Florida 32611
Chen-Sin Lin
Affiliation:
Industrial and Systems Engineering DepartmentThe University of Florida Gainesville, Florida 32611
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Abstract

In this paper we consider stochastic flow-shop scheduling with reference to certain lateness-related performance measures. We show that for various assumptions on the distribution of job-processing times of a flow shop, certain scheduling policies following the stochastic analogy of the Earliest Due Date (EDD) rule yield optimal results.

Type
Articles
Information
Probability in the Engineering and Informational Sciences , Volume 5 , Issue 2 , April 1991 , pp. 245 - 254
Copyright
Copyright © Cambridge University Press 1991

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References

Banerjee, B.P. (1965). Single facility sequencing with random execution times. Operations Research 13: 358–364.CrossRefGoogle Scholar
Boxma, O.J. & Forst, F.G. (1986). Minimizing the expected weighted number of tardy jobs in stochastic flow shops, Operations Research Letters 5: 119–126.Google Scholar
Brown, M. & Solomon, H. (1973). Optimal issuing policies under stochastic field lives. Journal of Applied Probability 10: 184–197.Google Scholar
Crabill, T.B. & Maxwell, W.L. (1969). Single machine sequencing with random processing times and random due-dates. Naval Research Logistics Quarterly 16: 549–554.CrossRefGoogle Scholar
Dempster, M.A.H., Lenstra, J.K. & Rinnooy, Kan A.H.G. (1981). Deterministic and stochastic scheduling. Proceedings of the NATO Advanced Study and Research Insitute on Theoretical Approaches to Scheduling Problems, Durham, England, 07 6–17. Dordrecht, Holland: D. Reidel.Google Scholar
Forst, F. G. (1984). A review of the static, stochastic job sequencing literature. Opsearch 21: 127–144.Google Scholar
Lawler, E.L., Lenstra, J.K., Rinnooy, Kan A.H.G. & Shmoys, D.B. (1989). Sequencing and scheduling: Algorithms and complexity. Report BS-R8909, Center for Mathematics and Computer Science, Amsterdam.Google Scholar
Pinedo, M. (1982). Minimizing the expected makespan in stochastic flow shops. Operations Research 30: 148–162.Google Scholar
Pinedo, M. (1983). Stochastic scheduling with release dates and due dates. Operations Research 31: 559–572.Google Scholar
Pinedo, M. (1984). Optimal policies in stochastic shop scheduling. Annals of Operations Research 1: 305–329.CrossRefGoogle Scholar
Pinedo, M. (1985). A note on stochastic shop models in which jobs have the same processing requirements on each machine. Management Science 31: 840–846.Google Scholar
Pinedo, M. & Wie, S.H. (1986). Inequalities for stochastic flow shops and job shops. Applied Stochastic Models and Data Analysis 2: 61–69.Google Scholar
Ross, S.M. (1983). Introduction to stochastic dynamic programming. Orlando, FL: Academic Press.Google Scholar
Smith, M.L., Dudek, R.A. & Blair, E.L. (1986). Characteristics of U.S. flexible manufacturing system-A survey. In Stecke, K.E. and Sun, R. (Eds.), Flexible manufacturing systems. Amsterdam: Elsevier.Google Scholar
Weiss, G. (1982). Multiserver stochastic scheduling. In Dempster, M.A.H., Lenstra, J.K., and Kan, A.H.G. Rinnooy (Eds.), Deterministic and stochastic scheduling. Boston: D. Reidel, pp. 157–179.Google Scholar