Abstract
An immune genetic algorithm based on bottleneck jobs is presented for the job shop scheduling problem in which the total weighted tardiness must be minimized. Bottleneck jobs have significant impact on final scheduling performance and therefore need to be considered with higher priority. In order to describe the characteristic information concerning bottleneck jobs, a fuzzy inference system is employed to transform human knowledge into the bottleneck characteristic values which reflect the features of both the objective function and the current optimization stage. Then, an immune operator is designed based on these characteristic values and a genetic algorithm combined with the immune mechanism is devised to solve the job shop scheduling problem. Numerical computations for problems of different scales show that the proposed algorithm achieves effective results by accelerating the convergence of the optimization process.
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
Lenstra, J.K., Kan, A.H.G.R., Brucker, P.: Complexity of machine scheduling problems. Annals of Discrete Mathematics 7, 343–362 (1977)
Cheng, R., Gen, M.: A tutorial survey of job-shop scheduling problems using genetic algorithms—Part I: representation. Computers & Industrial Engineering 34(4), 983–997 (1996)
Cheng, R., Gen, M.: A tutorial survey of job-shop scheduling problems using genetic algorithms—Part II: hybrid genetic search strategies. Computers & Industrial Engineering 36(2), 343–364 (1999)
Gao, J., Sun, L., Gen, M.: A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Computers & Operations Research (in press)
Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Optimization. Kluwer Academic Publishers, Boston (2002)
Jiao, L., Wang, L.: A novel genetic algorithm based on immunity. IEEE Transactions on Systems, Man and Cybernetics, Part A 30(5), 552–561 (2000)
Adams, J., Balas, E., Zawack, D.: The shifting bottleneck procedure for job shop scheduling. Management Science 34(3), 391–401 (1988)
Roser, C., Nakano, M., Tanaka, M.: Shifting bottleneck detection. In: Proceedings of the Winter Simulation Conference, pp. 1079–1086 (2002)
Varela, R., Vela, C.R., Puente, J., Gomez, A.: A knowledge-based evolutionary strategy for scheduling problems with bottlenecks. European Journal of Operational Research 145(1), 57–71 (2003)
Wu, S.D., Byeon, E.S., Storer, R.H.: A graph-theoretic decomposition of the job shop scheduling problem to achieve scheduling robustness. Operations Research 47(1), 113–124 (1999)
Jain, A.S., Meeran, S.: Deterministic job-shop scheduling: Past, present and future. European Journal of Operational Research 113(2), 390–434 (1999)
Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing. Prentice-Hall, Englewood Cliffs (1997)
Zhang, C.Y., Li, P.G., Rao, Y.Q., Li, S.X.: A new hybrid GA/SA algorithm for the job shop scheduling problem. In: Raidl, G.R., Gottlieb, J. (eds.) EvoCOP 2005. LNCS, vol. 3448, pp. 246–259. Springer, Heidelberg (2005)
Singer, M.: Decomposition methods for large job shops. Computers and Operations Research 28(3), 193–207 (2001)
Zhang, C.Y., Li, P.G., Rao, Y.Q., Guan, Z.L.: A very fast TS/SA algorithm for the job shop scheduling problem. Computers & Operations Research 35, 282–294 (2008)
Huang, K.L., Liao, C.J.: Ant colony optimization combined with taboo search for the job shop scheduling problem. Computers & Operations Research 35, 1030–1046 (2008)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, R., Wu, C. (2008). An Immune Genetic Algorithm Based on Bottleneck Jobs for the Job Shop Scheduling Problem. In: van Hemert, J., Cotta, C. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2008. Lecture Notes in Computer Science, vol 4972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78604-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-78604-7_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78603-0
Online ISBN: 978-3-540-78604-7
eBook Packages: Computer ScienceComputer Science (R0)