EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 45 (2022) ITM Web Conf., 45 (2022) 01033 References
Open Access
Issue
ITM Web Conf.
Volume 45, 2022
2021 3rd International Conference on Computer Science Communication and Network Security (CSCNS2021)
Article Number 01033
Number of page(s) 9
Section Computer Technology and System Design
DOI https://doi.org/10.1051/itmconf/20224501033
Published online 19 May 2022
  1. D. Lei, M. Li, and L. Wang, “A two-phase meta-heuristic for multiobjective flexible job shop scheduling problem with total energy consumption threshold, ” IEEE transactions on cybernetics, vol. 49, no. 3, pp. 1097–1109, 2018. [Google Scholar]
  2. X. Li and L. Gao, “An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem, ” International Journal of Production Economics, vol. 174, pp. 93-110, 2016. [CrossRef] [Google Scholar]
  3. Y. Demir and S. K. Isleyen, “Evaluation of mathematical models for flexible job-shop scheduling problems, ” Applied Mathematical Modelling, vol. 37, no. 3, pp. 977-988, 2013. [CrossRef] [MathSciNet] [Google Scholar]
  4. F. Pezzella, G. Morganti, and G. Ciaschetti, “A genetic algorithm for the flexible jobshop scheduling problem, ” Computers & operations research, vol. 35, no. 10, pp. 3202-3212, 2008. [CrossRef] [Google Scholar]
  5. M. Nouiri, A. Bekrar, A. Jemai, D. Trentesaux, A. C. Ammari, and S. Niar, “Two stage particle swarm optimization to solve the flexible job shop predictive scheduling problem considering possible machine breakdowns, ” Computers & Industrial Engineering, vol. 112, no. oct., pp. 595–606, 2017. [CrossRef] [Google Scholar]
  6. Ham, “Flexible job shop scheduling problem for parallel batch processing machine with compatible job families, ” Applied Mathematical Modelling, vol. 45, no. May, pp. 551–562, 2017. [CrossRef] [MathSciNet] [Google Scholar]
  7. H. Wang, Z. Jiang, Y. Wang, H. Zhang, and Y. Wang, “A two-stage optimization method for energy-saving flexible job-shop scheduling based on energy dynamic characterization, ” Journal of Cleaner Production, vol. 188, no. 1 JUL., pp. 575–588, 2018. [CrossRef] [Google Scholar]
  8. J. Li, X. Yuan, L. Bo, and Z. Zeng, “A pseudo-dynamic search ant colony optimization algorithm with improved negative feedback mechanism to solve tsp, ” 2018. [Google Scholar]
  9. X. Gu, M. Huang, and X. Liang, “An improved genetic algorithm with adaptive variable neighborhood search for fjsp, ” Algorithms, vol. 12, no. 11, p. 243, 2019. [CrossRef] [Google Scholar]
  10. Bagheri, M. Zandieh, I. Mahdavi, and M. Yazdani, “An artificial immune algorithm for the flexible job-shop scheduling problem, ” Future Generation Computer Systems, vol. 26, no. 4, pp. 533-541, 2010. [CrossRef] [Google Scholar]
  11. S. Saremi, S. Mirjalili, and A. Lewis, “Grasshopper optimisation algorithm: theory and application, ” Advances in Engineering Software, vol. 105, pp. 30-47, 2017. [CrossRef] [Google Scholar]
  12. J. Tian-Hua, “Flexible job shop scheduling problem with hybrid grey wolf optimization algorithm, “ Control and Decision, vol. 33, no. 3, pp. 503-508, 2018. [Google Scholar]
  13. X. Wu, X. Liu, and N. Zhao, “An improved differential evolution algorithm for solving a distributed assembly flexible job shop scheduling problem, ” Memetic Computing, vol. 11, no. 4, pp. 335-355, 2019. [CrossRef] [Google Scholar]
  14. B. Liu, L. Wang, and Y.-H. Jin, “An effective pso-based memetic algorithm for flow shop scheduling, ” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 37, no. 1, pp. 18–27, 2007. [CrossRef] [Google Scholar]
  15. Y. Yuan, H. Xu, and J. Yang, “A hybrid harmony search algorithm for the flexible job shop scheduling problem, ” Applied soft computing, vol. 13, no. 7, pp. 3259–3272, 2013. [CrossRef] [Google Scholar]
  16. Bagheri, M. Zandieh, I. Mahdavi, and M. Yazdani, “An artificial immune algorithm for the flexible job-shop scheduling problem, ” Future Generation Computer Systems, vol. 26, no. 4, pp. 533-541, 2010. [CrossRef] [Google Scholar]
  17. M. Dai, D. Tang, A. Giret, and M. A. Salido, “Multi-objective optimization for energyefficient flexible job shop scheduling problem with transportation constraints, ” Robotics and Computer-Integrated Manufacturing, vol. 59, pp. 143-157, 2019. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.