Abstract
This paper investigates the use of genetic programming in automatized synthesis of scheduling heuristics. The applied scheduling technique is priority scheduling, where the next state of the system is determined based on priority values of certain system elements. The evolved solutions are compared with existing scheduling heuristics for single machine dynamic problem and job shop scheduling with bottleneck estimation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jones, A., Rabelo, L.C.: Survey of job shop scheduling techniques. Technical report, NISTIR, National Institute of Standards and Technology, Gaithersburg (1998)
Walker, S.S., Brennan, R.W., Norrie, D.H.: Holonic job shop scheduling using a multiagent system. IEEE Intelligent Systems 2, 50 (2005)
Dimopoulos, C., Zalzala, A.: A genetic programming heuristic for the one-machine total tardiness problem. In: Proceedings of the Congress on Evolutionary Computation, vol. 3 (1999)
Dimopoulos, C., Zalzala, A.M.S.: Investigating the use of genetic programming for a classic one-machine scheduling problem. Advances in Engineering Software 32(6), 489 (2001)
Adams, T.P.: Creation of simple, deadline, and priority scheduling algorithms using genetic programming. In: Genetic Algorithms and Genetic Programming at Stanford 2002 (2002)
Yin, W.J., Liu, M., Wu, C.: Learning single-machine scheduling heuristics subject to machine breakdowns with genetic programming. In: Proceedings of the 2003 Congress on Evolutionary Computation CEC 2003, p. 1050. IEEE Press, Los Alamitos (2003)
Atlan, B.L., Polack, J.: Learning distributed reactive strategies by genetic programming for the general job shop problem. In: Proceedings 7th annual Florida Artificial Intelligence Research Symposium, IEEE Press, Los Alamitos (1994)
Miyashita, K.: Job-shop scheduling with gp. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2000), p. 505. Morgan Kaufmann, San Francisco (2000)
Cheng, V., Crawford, L., Menon, P.: Air traffic control using genetic search techniques. In: IEEE International Conference on Control Applications. IEEE, Hawai’i (1999)
Hansen, J.V.: Genetic search methods in air traffic control. Computers and Operations Research 31(3), 445 (2004)
Pinedo, M.: Offline deterministic scheduling, stochastic scheduling, and online deterministic scheduling: A comparative overview. In: Leung, J.Y.T. (ed.) Handbook of Scheduling, Chapman & Hall/CRC, Boca Raton (2004)
Morton, T.E., Pentico, D.W.: Heuristic Scheduling Systems. John Wiley & Sons, Inc, Chichester (1993)
Mohan, R., Rachamadugu, V., Morton, T.E.: Myopic heuristics for the weighted tardiness problem on identical parallel machines. Technical report, The Robotics Institute, Carnegie-Mellon University (1983)
Chang, Y.L., Sueyoshi, T., Sullivan, R.: Ranking dispatching rules by data envelopment analysis in a job shop environment. IIE Transactions 28(8), 631 (1996)
Taillard, E.: Scheduling instances (2003), http://ina.eivd.ch/Collaborateurs/etd/problemes.dir/ordonnancement.dir/ordonnancement.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jakobović, D., Budin, L. (2006). Dynamic Scheduling with Genetic Programming. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds) Genetic Programming. EuroGP 2006. Lecture Notes in Computer Science, vol 3905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11729976_7
Download citation
DOI: https://doi.org/10.1007/11729976_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33143-8
Online ISBN: 978-3-540-33144-5
eBook Packages: Computer ScienceComputer Science (R0)