Abstract
GENET is a local search approach with a neural network connectionist architecture for solving constraint satisfaction problems by iterative improvement and incorporates a learning strategy to escape local minima. In this paper, a method within the framework of propagation of posted new constraints and based on the progressive stochastic search of GENET for solving the job shop scheduling constraint satisfaction optimization problem (JSSCSOP) will be presented. The experimental results show that the performance of our method gets competitive when the domain of each variable is not big, even if the size of the problem instances increases.
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
Garey, M., Johnson, D.: Complexity Results for Multiprocessor Scheduling under Resource Constraints. SIAM Journal on Computing 4(4), 397–411 (1975)
Jain, A.S., Meeran, S.: Deterministic Job Shop Scheduling: Past, Present and Future. European Journal of Operational Research 113, 390–434 (1999)
Backer, B., Furnon, V., Shaw, P., Kilby, P., Prosser, P.: Solving Vehicle Routing Problems Using Constraint Programming and Meta-heuristics. Journal of Heuristics 6(4), 501–523 (2000)
Jussien, N., Lhomme, O.: Local Search With Constraint Propagation and Conflict-based Heuristics. Artificial Intelligence 139, 21–45 (2002)
Tsang, E.P.K., Wang, C.J.: A Generic Neural Network Approach for Constraint Satisfaction Problems. In: Neural Network Applications, pp. 12–22. Springer, Heidelberg (1992)
Davenport, A., Tsang, E., Wang, C.J., Zhu, K.: Genet: A Connectionist Architecture for Solving Constraint Satisfaction Problems by Iterative Improvement. In: Proc. 12th National Conference on Artificial Intelligence (1994)
Lee, J.H.M., Leung, H.F., Won, H.W.: Towards a More Efficient Stochastic Constraint Solver. In: Proc. 2th International Conference on Principles and Practice of Constraint Programming, pp. 338–352 (1996)
Lam, B.C.H., Leung, H.F.: Progressive Stochastic Search for Solving Constraint Satisfaction Problems. In: Proc. 15th IEEE International Conference on Tools with Artificial Intelligence (2003)
Choi, M.F., Lee, H.M., Stuckey, J.: A Lagrangian Reconstruction of GENET. Artificial Intelligence 123, 1–39 (2000)
Taillard, E.: Benchmarks for Basic Scheduling Problems. European Journal of Operational Research 64, 278–285 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Feng, X., Tang, L., Leung, H. (2004). Applying GENET to the JSSCSOP. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_76
Download citation
DOI: https://doi.org/10.1007/978-3-540-28647-9_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
eBook Packages: Springer Book Archive