Open Access
Issue
MATEC Web Conf.
Volume 100, 2017
13th Global Congress on Manufacturing and Management (GCMM 2016)
Article Number 02047
Number of page(s) 8
Section Part 2: Internet +, Big data and Flexible manufacturing
DOI https://doi.org/10.1051/matecconf/201710002047
Published online 08 March 2017
  1. D. Lei. Co-evolutionary genetic algorithm for fuzzy flexible job shop scheduling. Applied Soft Computing, 12,2237–2245(2012). [CrossRef] [Google Scholar]
  2. C.F.M. Toledo, P.M. França, R. Morabito, et al. Multi-population genetic algorithm to solve the synchronized and integrated two-level lot sizing and scheduling problem. International Journal of Production Research, 47,3097–3119(2009). [CrossRef] [Google Scholar]
  3. W. Teekeng, A. Thammano. Modified Genetic Algorithm for Flexible Job-Shop Scheduling Problems. Procedia Computer Science, 12,122–128(2012). [CrossRef] [Google Scholar]
  4. A. Jalilvand-Nejad, P. Fattahi. A mathematical model and genetic algorithm to cyclic flexible job shop scheduling problem. Journal of Intelligent Manufacturing, 26,1085–1098(2013). [CrossRef] [Google Scholar]
  5. A.J. Liu, Y. Yang, Q.S. Xing, et al. Multi-population genetic algorithm in multiobjective fuzzy and flexible Job Shop scheduling. Computer Integrated Manufacturing Systems, 17,1954–1961(2011). [Google Scholar]
  6. E.M. Zhou, X.J. Peng, J.Q. Wang, et al. Visual Simulation of Dual Clutch Transmission Assmbly Process based on DELMIA. Journal of Mechanical Transmission, 37,65–67(2013). [Google Scholar]
  7. Q. Zhang, H. Manier, M.A. Manier. A genetic algorithm with tabu search procedure for flexible job shop scheduling with transportation constraints and bounded processing times. Computers & Operations Research, 39,1713–1723(2012). [CrossRef] [Google Scholar]
  8. X. Li, L. Gao. An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem. International Journal of Production Economics, 174,93–110(2016). [CrossRef] [Google Scholar]
  9. R. Zhang, R. Chiong. Solving the energy-efficient job shop scheduling problem: A multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption. Journal of Cleaner Production, 112,3361–3375(2015). [CrossRef] [Google Scholar]
  10. J.C. Chen, C.C. Wu, C.W. Chen, et al. Flexible job shop scheduling with parallel machines using Genetic Algorithm and Grouping Genetic Algorithm. Expert Systems with Applications, 39,10016–10021(2012). [CrossRef] [Google Scholar]
  11. N. Kundakcı, O. Kulak. Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem. Computers & Industrial Engineering, 96,31–51(2016). [CrossRef] [Google Scholar]
  12. C. Gutiérrez, I. García-Magariño. Modular design of a hybrid genetic algorithm for a flexible job –shop scheduling problem. Knowledge-Based Systems, 24,102–112(2011). [CrossRef] [Google Scholar]
  13. J. Gao, M. Gen, L. Sun, et al. A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems . Computers & Industrial Engineering, 53,149–162(2007). [CrossRef] [Google Scholar]
  14. R.K. Phanden, A. Jain, R. Verma. A Genetic Algorithm-Based Approach for Flexible Job Shop Scheduling. Applied Mechanics & Materials, 2011, 7,3930–3937(2011). [CrossRef] [Google Scholar]
  15. M. Zandieh, I. Mahdavi, A. Bagheri. Solving the Flexible Job-Shop Scheduling Problem by a Genetic Algorithm. Journal of Applied Sciences, 8,4650–4655(2008). [CrossRef] [Google Scholar]
  16. J. Gao, L. Sun, M. Gen. A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Computers & Operations Research, 35,2892–2907(2008). [CrossRef] [Google Scholar]
  17. Z.M. Bzymek, M. Nunez, M. Li, et al. Simulation of a Machining Sequence Using Delmia/Quest Software. Computer-Aided Design and Applications, 5,401–411(2013). [CrossRef] [Google Scholar]
  18. E.M. Zhou, J. Zhu, G.Y. Wang. DELMIA/QUEST software-based digital process planning production line research. Manufacturing Automation, 37,1–4(2015). [Google Scholar]
  19. C. Guiling, L. Chuan, G. Jie, et al. Virtual human control technology for immersed virtual maintenance system in DELMIA environment: 2010 International Conference on Audio, Language and Image Processing(2010) [Google Scholar]
  20. G. Pasca, I. Maniu. Synthesis of the design of flexible manufacturing system using Delmia/Quest software. Annals of Daaam & Proceedings, 695–697(2009). [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.