Abstract
The dynamic sequencing of jobs through a multiprocessor system is one to which little attention has been paid, in contrast to the extensive literature on static sequencing problems. Yet in many real problems, to assume, as the static model does, that we know about all jobs that will arrive in the course of a processing cycle is hardly realistic.
Existing solutions usually assume a queueingtheoretic orientation, rather than an optimization one, in which the decision as to which job should be processed is made on the basis of some simple selection criteria, such as First-Come First-Served, or Shortest Processing Time.
Here we investigate the use of a Genetic Algorithm (GA) to solve the successive sequencing problems generated by finding a near-optimal sequence for those jobs available just before successive event times—the times at which the job being processed on the first processor completes its processing. Some comparisons are made between using the GA approach versus some simple rules.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
On sabbatical leave in Dept. of Computer Science, City University, London, for the period of this research.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
G.A. Cleveland and S.F. Smith (1989) Using genetic algorithms to schedule flow shop releases. In J.D. Schaffer (Ed.) (1989) Proceedings of the 3rd International Conference on Genetic Algorithms. Morgan Kaufmann, Los Altos, CA.
H.M. Cartwright and G.F. Mott (1991) Looking around: using clues from the data space to guide genetic algorithm searches. In R.K. Belew and L.B. Booker (Eds.) (1991) Proceedings of the 4th International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA.
C.R. Reeves (1993) A genetic algorithm for flowshop sequencing. Computers & Ops. Res., (in review).
C.R. Reeves (1992) A genetic algorithm approach to stochastic flowshop sequencing. Proc. IEE Colloquium on Genetic Algorithms for Control and Systems Engineering. Digest No.1992/106, IEE, London.
S.C. Graves (1981) A review of production scheduling. Operations Research, 29, 646–675.
R.A. Dudek, S.S. Panwalkar and M.L. Smith (1992) The lessons of flowshop scheduling research. Operations Research, 40, 7–13.
R.W. Conway, W.L. Maxwell and L.W. Miller (1967) Theory of Scheduling. Addison-Wesley, Reading, Mass.
C.H. Sauer and K.M. Chandy (1981) Computer Systems Performance Modelling. Prentice-Hall, New Jersey.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1993 Springer-Verlag/Wien
About this paper
Cite this paper
Reeves, C., Karatza, H. (1993). Dynamic Sequencing of A Multi-Processor System: A Genetic Algorithm Approach. In: Albrecht, R.F., Reeves, C.R., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7533-0_71
Download citation
DOI: https://doi.org/10.1007/978-3-7091-7533-0_71
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82459-7
Online ISBN: 978-3-7091-7533-0
eBook Packages: Springer Book Archive