Abstract
In this paper, a discrete state transition algorithm is introduced to solve a multiobjective single machine job shop scheduling problem. In the proposed approach, a non-dominated sort technique is used to select the best from a candidate state set, and a Pareto archived strategy is adopted to keep all the non-dominated solutions. Compared with the enumeration and other heuristics, experimental results have demonstrated the effectiveness of the multiobjective state transition algorithm.
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 subscriptionsReferences
Marler, R.T., Arora, J.S.: Survey of multi-objective optimization methods for engineering. Struct. Multidiscip. Optim. 26(6), 369–395 (2004)
Coello, C.A.C.: A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowl. Inf. Syst. 1(3), 129–156 (1999)
Lei, D.M.: A Pareto archive particle swarm optimization for multi-objective job shop scheduling. Comput. Ind. Eng. 54(4), 960–971 (2008)
Al-Anzi, F.S., Allahverdi, A.: A self-adaptive differential evolution heuristic for two-stage assembly scheduling problem to minimize maximum lateness with setup times. Eur. J. Oper. Res. 182(1), 80–94 (2007)
Xia, W.J., Wu, Z.M.: An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Comput. Ind. Eng. 48(2), 409–425 (2005)
Hanoun, S., Nahavandi, S., Kull, H.: Pareto archived simulated annealing for single machine job shop scheduling with multiple objectives. In: The Sixth International Multi-Conference on Computing in the Global Information Technology (ICCGI), pp. 99–104 (2011)
Hanoun, S., Creighton, D., Nahavandi, S. Kull, H.: Solving a multiobjective job shop scheduling problem using Pareto Archived Cuckoo Search. In: Proceedings of the 2012 17th IEEE International Conference on Emerging Technologies & Factory Automation, pp. 1–8 (2012)
Zhou, X.J., Yang, C.H., Gui, W.H.: Initial version of state transition algorithm. In: International Conference on Digital Manufacturing and Automation (ICDMA), pp. 644–647 (2011)
Zhou, X.J., Yang, C.H., Gui, W.H.: A new transformation into state transition algorithm for finding the global minimum. In: International Conference on Intelligent Control and Information Processing (ICICIP), pp. 674–678 (2011)
Yang, C.H., Tang, X.L., Zhou, X.J., Gui, W.H.: State transition algorithm for traveling salesman problem. In: the Proceedings of the 31st Chinese Control Conference (CCC), pp. 2481–2485 (2012)
Zhou, X.J., Yang, C.H., Gui, W.H.: State transition algorithm. J. Ind. Manag. Optim. 8(4), 1039–1056 (2012)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Acknowledgements
This research work is conducted between Deakin University and Ballarat University under the Collaboration Research Network (CRN) initiative. The problem studied in this paper is related to the Australian Research Council (ARC) linkage project number LP0991175.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhou, X., Hanoun, S., Gao, D.Y., Nahavandi, S. (2015). A Multiobjective State Transition Algorithm for Single Machine Scheduling. In: Gao, D., Ruan, N., Xing, W. (eds) Advances in Global Optimization. Springer Proceedings in Mathematics & Statistics, vol 95. Springer, Cham. https://doi.org/10.1007/978-3-319-08377-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-08377-3_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08376-6
Online ISBN: 978-3-319-08377-3
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)