EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 253 (2021) E3S Web Conf., 253 (2021) 02024 References
Open Access
Issue
E3S Web Conf.
Volume 253, 2021
2021 International Conference on Environmental and Engineering Management (EEM 2021)
Article Number 02024
Number of page(s) 5
Section Big Data Environment Management Application and Industry Research
DOI https://doi.org/10.1051/e3sconf/202125302024
Published online 06 May 2021
  1. Johnson S.M.. Optimal two and three-stage production schedules with setup times included[J]. Naval Research Logistics Quarterly, 1954, 1(1): 61–68. [Google Scholar]
  2. Adams J., Balas E., Zawack D.. The shifting bottleneck procedure for job shop scheduling[J]. Management Science, 1988, 34(3): 391–401. [Google Scholar]
  3. Davis L.. Job shop scheduling with genetic algorithms[C]// Proceedings of an International Conference on Genetic Algorithms and Their Applications, 1985: 136–140. [Google Scholar]
  4. Goncalves J.F., Magalhaes Mendes J.J., Resende M.G.C.. A hybrid genetic algorithm for the job shop scheduling problem [J]. European Journal of Operational Research, 2005, 167 (1): 77–95 [Google Scholar]
  5. Zhang C.Y., Rao Y.Q., Li P.G.. An effective hybrid genetic algorithm for the job shop scheduling problem[J]. International Journal of Advanced Manufacturing Technology, 2008, 39: 965–974. [Google Scholar]
  6. Yildirim M.B., Mouzon G.. Single-Machine Sustainable Production Planning to Minimize Total Energy Consumption and Total Completion Time Using a Multiple Objective Genetic Algorithm[J]. IEEE Transactions on Engineering Management, 2012, 59(4): 585–597 [Google Scholar]
  7. Goncalves J.F., Resende M.G.C.. An extended akers graphical method with a biased randon-key genetic algorithm for job shop scheduling[J]. International Transactions in Operational Research, 2014, 21(2): 215–246. [Google Scholar]
  8. Taillard E.D.. Parallel taboo search techniques for the job shop scheduling problem[J]. ORSA Journal on Computing, 1994, 2(6): 108–117. [Google Scholar]
  9. Nowicki E., Smutnicki C.. A fast taboo search algorithm for the job shop problem[J]. Management Science, 1996, 42 (6): 797–813 [Google Scholar]
  10. Nowicki E., Smutnicki C.. Some new tools to solve the job shop problem[R]. Institute of Engineering Cybernetics, Wroclaw University of Technology, 2002. [Google Scholar]
  11. Zhang C.Y., Li P.G., Guan Z.L., et al. A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem[J]. Computers & Operations Research, 2007, 34(11): 3229–3242. [Google Scholar]
  12. Zhang C.Y., Li P.G., Raoy Q., et al. A very fast TS /SA algorithm for the job shop scheduling problem[J]. Computers&Operations Research, 2008, 35(1): 282–294. [Google Scholar]
  13. Nasiri M.M., Kianfar F.. A GES/TS algorithm for the job shop scheduling[J]. Computers & Industrial Engineering, 2012, 62(4): 946–952. [Google Scholar]
  14. Peng B., Lu Z., Cheng T.C.E.. A tabu search/path relinking algorithm to solve the job shop scheduling problem[J]. Computers & Operations Research, 2015, 53: 154–164. [Google Scholar]
  15. Alharkan I., Saleh M., Ghaleb M.A., et al. Tabu search and particle swarm optimization algorithms for two identical parallel machines scheduling problem with a single server[J]. Journal of King Saud University-Engineering Sciences. 2019. [Google Scholar]
  16. Matsuo H., Suh C.J., Sullivan R.S.. A controlled search simulated annealing method for the general job-shop scheduling problem[R]. Graduate School of Business, University of Texas at Austin, 1988. [Google Scholar]
  17. Van Laarhoven P.J.M., Aarts E.H.L., Lenstra J.K.. Job shop scheduling by simulated annealing [J]. Operations Research, 1992, 40(1): 113–125. [Google Scholar]
  18. Jaroslaw P., Czeslaw S., Dominik Z. Optimizing Bicriteria Flow Shop Scheduling Problem by Simulated Annealing Algorithm[J]. Procedia Computer Science. 2013, 18:936–945. [Google Scholar]
  19. Jolai F., Asefi H., Rabiee M., et al. Bi-objective simulated annealing approaches for no-wait two-stage flexible flow shop scheduling problem[J]. Scientia Iranica. 2013, 20(3): 861–872. [Google Scholar]
  20. Akram K., Kamal K., Zeb A. Fast simulated annealing hybridized with quenching for solving job shop scheduling problem[J]. Applied Soft Computing. 2016, 49: 510–523. [Google Scholar]
  21. Ponsich A., Coello Coello C.A.. A hybrid differential evolution-tabu search algorithm for the solution of job-shop scheduling problems[J]. Applied Soft Computing, 2013, 13(1): 462–474. [Google Scholar]
  22. Cao Y., Song X., Zhang Y., et al. Application of ACOTS hybrid algorithm for job shop scheduling problems[C] // 2012 Proceedings of International Conference on Modelling, Identification &Control, IEEE, 2012: 289–293. [Google Scholar]
  23. Zhang R., Song S., Wu C.. A hybrid artificial bee colony algorithm for the job shop scheduling problem[J]. International Journal of Production Economics, 2013, 141(1): 167–178. [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.