Abstract
Genetic algorithms (GAs) have been found to be suitable for solving Job-Shop Scheduling Problem (JSSP). However, convergence in GAs is rather slow and thus new GA structures and techniques are currently widely investigated. In this paper, we propose to solve JSSP using distributed micro-genetic algorithm (micro-GA) with local search based on the Asynchronous Colony Genetic Algorithms (ACGA). We also developed a representation for the problem in order to refine the schedules using schedule builder which can change a semi-active schedule to active schedule. The proposed technique is applied to Muth and Thompson’s 10x10 and 20x 5 problems as well as a real world JSSP. The results show that the distributed micro GA is able to give a good optimal makespan in a short time as compared to the manual schedule built for the real world JSSP.
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
Muth, J., Thompson, G.: Industrial Scheduling. Prentice Hall, Englewood Cliffs (1963)
Giffler, B., Thompson, J.L.: Algorithms for Solving Production-Scheduling Problems. Operations Research 8, 487–503 (1960)
Carlier, J., Pinson, E.: An Algorithm for Solving Job-Shop Problem. Management Science 35, 164–176 (1989)
Kubota, A.: Study On Optimal Scheduling for Manufacturing System by Genetic Algorithms. Ashikaga Institute of Technology: Master Thesis (1995)
Holsapple, C., Jacob, V., Pakath, R., Zaveri, J.: A Genetics-Based Hybrid Scheduler for Generating Static Schedules in Flexible Manufacturing Contexts. IEEE Transactions on System, Man, and Cybernetics 23, 953–971 (1993)
Yamada, T., Nakano, R.: Genetic Algorithms for Job-Shop-Scheduling Problems. In: Proceedings of Modern Heuristic for Decision Support, UNICOM seminar, London, pp. 67–81 (1997)
Dorndorf, U., Pesch, E.: Evolution Based Learning in A Job Shop Scheduling Environment. Computers Ops. Res. 22, 25–40 (1995)
Zhang, H., Chen, R.: Research on Coarse-grained Parallel Genetic Algorithm Based Grid Job Scheduling. In: Proceedings of the Fourth International Conference on Semantics, Knowledge and Grid, pp. 505–506 (2008)
Kirley, M.: A Coevolutionary Genetic Algorithm for Job Scheduling Problems. In: Proceedings of the 1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems, pp. 84–87 (1999)
Defersha, F.M., Chen, M.: A Coarse-Grain Parallel Genetic Algorithm for Flexible Job-Shop Scheduling with Lot Streaming. In: Proceedings of International Conference on Computational Science and Engineering, 2009, pp. 201–208 (2009)
Park, B.J., Choi, H.R., Kim, H.S.: A hybrid genetic algorithm for the job shop scheduling problems. Computers & Industrial Engineering 45, 597–613 (2003)
Inoue, H., Funyu, Y., Kishino, K., Jinguji, T., Shiozawa, M., Yoshikawa, S., Nakao, T.: Development of Artificial Life Based Optimization System. In: Proceedings of the Eighth International Conference on Parallel and Distributed Systems, pp. 429–436 (2001)
Krishnakumar, K.: Micro-genetic algorithms for stationary and non-stationary function optimization. In: SPIE Proceedings Intelligent Control and Adaptive Systems, pp. 289–296 (1989)
Goldberg, D.: Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Menlo Park (1988)
Bierwirth, C.: A Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms. OR Spektrum 17, 87–92 (1995)
Merz, P., Freisleben, B.: A Genetic Local Search Approach to the Quadratic Assignment Problem. In: Proceedings of the Seventh International Conference on Genetic Algorithms, ICGA 1997 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yusof, R., Khalid, M., San, T.C. (2010). Solving Industrial Based Job-Shop Scheduling Problem by Distributed Micro-Genetic Algorithm with Local Search. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15387-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-15387-7_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15386-0
Online ISBN: 978-3-642-15387-7
eBook Packages: Computer ScienceComputer Science (R0)