Abstract
In this paper, an effective artificial bee colony (ABC) algorithm is proposed to solve the multi-objective flexible job-shop scheduling problem with the criteria to minimize the maximum completion time, the total workload of machines and the workload of the critical machine simultaneously. By using the effective decoding scheme, hybrid initialization strategy, crossover and mutation operators for machine assignment and operation sequence, local search based on critical path and population updating strategy, the exploration and exploitation abilities of ABC algorithm are stressed and well balanced. Simulation results based on some widely used benchmark instances and comparisons with some existing algorithms demonstrate the effectiveness of the proposed ABC algorithm.
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
Bruker, P., Schlie, R.: Job-shop Scheduling with Multi-purpose Machines. Computing 45(4), 369–375 (1990)
Kacem, I., Hammadi, S., Borne, P.: Pareto-optimality Approach for Flexible Job-shop Scheduling Problems: Hybridization of evolutionary algorithms and fuzzy logic. Mathematics and Computers in Simulation 60(3-5), 245–276 (2002)
Kacem, I., Hammadi, S., Borne, P.: Approach by Localization and Multi-objective Evolutionary Optimization for Flexible Job-shop Scheduling Problems. IEEE Trans. on Systems, Man and Cybernetics, Part C 32(1), 1–13 (2002)
Zhang, G.H., Shao, X.Y., Li, P.G., Gao, L.: An Effective Hybrid Particle Swarm Optimization Algorithm for Multi-objective Flexible Job-shop Scheduling Problem. Comput. Ind. Eng. 56(4), 1309–1318 (2009)
Xing, L.N., Chen, Y.W., Yang, K.W.: An Efficient Search Method for Multi-objective Flexible Job Shop Scheduling Problems. Journal of Intelligent Manufacturing 20(3), 283–293 (2009)
Li, J.Q., Pan, Q.K., Liang, Y.C.: An Effective Hybrid Tabu Search Algorithm for Multi-objective Flexible Job-shop Scheduling Problems. Computers & Industrial Engineering 59(4), 647–662 (2010)
Wang, X.J., Gao, L., Zhang, C.Y., Shao, X.Y.: A Multi-objective Genetic Algorithm Based on Immune and Entropy Principle for Flexible Job-shop Scheduling Problem. International J. of Advanced Manufacturing Technology 51(5-8), 757–767 (2010)
Brandimarte, P.: Routing and Scheduling in A Flexible Job Shop by Tabu Search. Annals of Operations Research 41(3), 157–183 (1993)
Karaboga, D.: An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report TR06, Computer Engineering Department, Erciyes University, Turkey (2005)
Baykasoglu, A., Ozbakir, L., Tapkan, P.: Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem. In: Swarm Intelligence Focus on Ant and Particle Swarm Optimization, pp. 113–144. I-Tech Education and Publishing, Vienna (2007)
Pezzella, F., Morganti, G., Ciaschetti, G.: A Genetic Algorithm for the Flexible Job-shop Scheduling Problem. Computers & Operations Research 35(10), 3202–3212 (2008)
Watanabe, M., Ida, K., Gen, M.: A Genetic Algorithm with Modified Crossover Operator and Search Area Adaptation for the Job-shop Scheduling Problem. Computers & Industrial Engineering 48(4), 743–752 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, G., Wang, L., Xu, Y., Wang, S. (2012). An Effective Artificial Bee Colony Algorithm for Multi-objective Flexible Job-Shop Scheduling Problem. In: Huang, DS., Gan, Y., Gupta, P., Gromiha, M.M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2011. Lecture Notes in Computer Science(), vol 6839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25944-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-25944-9_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25943-2
Online ISBN: 978-3-642-25944-9
eBook Packages: Computer ScienceComputer Science (R0)