Abstract
Aiming at the needs of the job shop to arrange production scheduling reasonably in the actual industry, an improved particle swarm optimization method is proposed to solve the job shop scheduling problem (JSSP). By analyzing the scheduling characteristics of the job shop and according to various resource constraints, a process-based coding and activity scheduling decoding mechanism suitable for particle swarm optimization is designed. Use the proportional mutation strategy and the ring topology structure to improve the traditional particle swarm optimization algorithm, and combine the elite selection learning strategy to retain excellent individual information which accelerate the convergence speed. The improved particle swarm optimization algorithm is used to solve standard instance problems of different scales, and the effectiveness of the algorithm is verified by comparing with other methods.
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
References
Nouiri, M., Bekrar, A., Jemai, A., Niar, S., Ammari, A.C.: An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem. J. Intell. Manuf. 29(3), 603–615 (2015). https://doi.org/10.1007/s10845-015-1039-3
Huang, R.H., Yu, T.H.: An effective ant colony optimization algorithm for multi-objective job-shop scheduling with equal-size lot-splitting. Appl. Soft Comput. 57, 642–656 (2017)
Sharma, N., Sharma, H., Sharma, A.: Beer froth artificial bee colony algorithm for job-shop scheduling problem. Appl. Soft Comput. 68, 507–524 (2018)
Jamrus, T., Chien, C.F., Gen, M., et al.: Hybrid particle swarm optimization combined with genetic operators for flexible job-shop scheduling under uncertain processing time for semiconductor manufacturing. IEEE Trans. Semicond. Manuf. 99, 1036–1052 (2017)
Hao, G., Kwong, S., Fan, B., et al.: A hybrid particle-swarm tabu search algorithm for solving job shop scheduling problems. IEEE Trans. Ind. Inf. 10, 2044–2054 (2017)
Abdel-Kader, R.F.: An improved PSO algorithm with genetic and neighborhood-based diversity operators for the job shop scheduling problem. Appl. Artif. Intell. 32(5), 433–462 (2018). https://doi.org/10.1080/08839514.2018.1481903
Zheng, X.L., Wang, L.: A collaborative multi-objective fruit fly optimization algorithm for the resource constrained unrelated parallel machine green scheduling problem. IEEE Trans. Syst. Man Cybern.: Syst. 48(5), 790–800 (2018)
Cruz-Chávez, M.A.: Neighbourhood generation mechanism applied in simulated annealing to job shop scheduling problems. Int. J. Syst. Sci. 46(15), 2673–2685 (2015). https://doi.org/10.1080/00207721.2013.876679
Spanos, A.C., Ponis, S.T., Tatsiopoulos, I.P., et al.: A new hybrid parallel genetic algorithm for the job-shop scheduling problem. Int. Trans. Oper. Res. 21(3), 479–499 (2014)
Li, Y., et al.: Intelligent prediction of private information diffusion in social networks. Electronics 9(5), 719 (2020)
Zhang, L., Li, Y., Liao, Y., Kong, R., Wu, W.: An empirical exploration of intelligence system based big data. J. CAEIT 11(6), 608–613 (2016)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, G., Wu, T. (2021). Application Research of Improved Particle Swarm Optimization Algorithm Used on Job Shop Scheduling Problem. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-78609-0_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78608-3
Online ISBN: 978-3-030-78609-0
eBook Packages: Computer ScienceComputer Science (R0)