Abstract
It is well known, the Job-Shop Scheduling Problem (JSSP) is the most complicated and typical problem of all kinds of production scheduling problems, the allocation of resources over time to perform a collection of tasks. The current method has several shortcomings in solving the JSSP. In this paper, we correct these deficiencies by introducing a dynamic model that is based on an analysis of the run-time behavior of CSANN algorithm. At the same time, this paper proposes several new heuristics in order to improve the performance of CSANN. The computational simulations have shown that the proposed hybrid approach has good performance with respect to the quality of solution and the speed of computation.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Baker, K.R.: Introduction to Sequence and Scheduling. Wiley, New York (1974)
Garey, M.R., Johnson, D.S., Sethi, R.: The Complexity of Flow-Shop and Job-Shop Scheduling. Mathematics of Operations Research 1(2), 117–129 (1976)
Erschler, J.F., Roubellat, J.P.: Vernhes: Finding Some Essential Characteristics of the Feasible Solutions for a Scheduling Problem. Operations Research 24(4), 774–783 (1976)
French, S.: Sequencing and Scheduling: an Introduction to the Mathematics of the Job-Shop. Wiley, New York (1982)
Yang, S., Wang, D.: Constraint Satisfaction Adaptive Neural Network and Heuristics Combined Approaches for Generalized Job-Shop Scheduling. IEEE Transactions on Neural Networks 11(2), 474–486 (2000)
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
Xing, LN., Chen, YW., Shen, XS. (2006). A Constraint Satisfaction Adaptive Neural Network with Dynamic Model for Job-Shop Scheduling Problem. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_135
Download citation
DOI: https://doi.org/10.1007/11760191_135
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
Online ISBN: 978-3-540-34483-4
eBook Packages: Computer ScienceComputer Science (R0)