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An effective architecture for learning and evolving flexible job-shop schedules

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  • Ho, Nhu Binh
  • Tay, Joc Cing
  • Lai, Edmund M.-K.

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  • Ho, Nhu Binh & Tay, Joc Cing & Lai, Edmund M.-K., 2007. "An effective architecture for learning and evolving flexible job-shop schedules," European Journal of Operational Research, Elsevier, vol. 179(2), pages 316-333, June.
    • Handle: RePEc:eee:ejores:v:179:y:2007:i:2:p:316-333
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    References listed on IDEAS

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    1. Kacem, Imed & Hammadi, Slim & Borne, Pierre, 2002. "Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 60(3), pages 245-276.
    2. Eugeniusz Nowicki & Czeslaw Smutnicki, 1996. "A Fast Taboo Search Algorithm for the Job Shop Problem," Management Science, INFORMS, vol. 42(6), pages 797-813, June.
    3. Kolonko, M., 1999. "Some new results on simulated annealing applied to the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 113(1), pages 123-136, February.
    4. Fortemps, Ph. & Ost, Ch. & Pirlot, M. & Teghem, J. & Tuyttens, D., 1996. "Using metaheuristics for solving a production scheduling problem in a chemical firm. A case study," International Journal of Production Economics, Elsevier, vol. 46(1), pages 13-26, December.
    5. Varela, Ramiro & Vela, Camino R. & Puente, Jorge & Gomez, Alberto, 2003. "A knowledge-based evolutionary strategy for scheduling problems with bottlenecks," European Journal of Operational Research, Elsevier, vol. 145(1), pages 57-71, February.
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    Cited by:

    1. Seyed Habib A. Rahmati & Abbas Ahmadi & Kannan Govindan, 2018. "A novel integrated condition-based maintenance and stochastic flexible job shop scheduling problem: simulation-based optimization approach," Annals of Operations Research, Springer, vol. 269(1), pages 583-621, October.
    2. Václavík, Roman & Novák, Antonín & Šůcha, Přemysl & Hanzálek, Zdeněk, 2018. "Accelerating the Branch-and-Price Algorithm Using Machine Learning," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1055-1069.
    3. Zhang, Sicheng & Li, Xiang & Zhang, Bowen & Wang, Shouyang, 2020. "Multi-objective optimisation in flexible assembly job shop scheduling using a distributed ant colony system," European Journal of Operational Research, Elsevier, vol. 283(2), pages 441-460.
    4. Zhang, Zhe & Song, Xiaoling & Huang, Huijung & Zhou, Xiaoyang & Yin, Yong, 2022. "Logic-based Benders decomposition method for the seru scheduling problem with sequence-dependent setup time and DeJong’s learning effect," European Journal of Operational Research, Elsevier, vol. 297(3), pages 866-877.
    5. Miguel S. E. Martins & Joaquim L. Viegas & Tiago Coito & Bernardo Firme & Andrea Costigliola & João Figueiredo & Susana M. Vieira & João M. C. Sousa, 2023. "Minimizing total completion time in large-sized pharmaceutical quality control scheduling," Journal of Heuristics, Springer, vol. 29(1), pages 177-206, February.
    6. Jose L. Andrade-Pineda & David Canca & Pedro L. Gonzalez-R & M. Calle, 2020. "Scheduling a dual-resource flexible job shop with makespan and due date-related criteria," Annals of Operations Research, Springer, vol. 291(1), pages 5-35, August.
    7. Sweeney, Kevin D. & Sweeney, Donald C. & Campbell, James F., 2019. "The performance of priority dispatching rules in a complex job shop: A study on the Upper Mississippi River," International Journal of Production Economics, Elsevier, vol. 216(C), pages 154-172.
    8. Vilcot, Geoffrey & Billaut, Jean-Charles, 2008. "A tabu search and a genetic algorithm for solving a bicriteria general job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 190(2), pages 398-411, October.
    9. Alper Türkyılmaz & Özlem Şenvar & İrem Ünal & Serol Bulkan, 2020. "A research survey: heuristic approaches for solving multi objective flexible job shop problems," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1949-1983, December.
    10. Hao-Chin Chang & Tung-Kuan Liu, 2017. "Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1973-1986, December.
    11. Li Zhou & Zhuoning Chen & Shaoping Chen, 2018. "An effective detailed operation scheduling in MES based on hybrid genetic algorithm," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 135-153, January.
    12. Shen, Liji & Dauzère-Pérès, Stéphane & Neufeld, Janis S., 2018. "Solving the flexible job shop scheduling problem with sequence-dependent setup times," European Journal of Operational Research, Elsevier, vol. 265(2), pages 503-516.
    13. Li, Xinyu & Gao, Liang, 2016. "An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 174(C), pages 93-110.
    14. González, Miguel A. & Vela, Camino R. & Varela, Ramiro, 2015. "Scatter search with path relinking for the flexible job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 245(1), pages 35-45.
    15. Zhang, Zhe & Gong, Xue & Song, Xiaoling & Yin, Yong & Lev, Benjamin & Chen, Jie, 2022. "A column generation-based exact solution method for seru scheduling problems," Omega, Elsevier, vol. 108(C).
    16. Abderraouf Maoudj & Brahim Bouzouia & Abdelfetah Hentout & Ahmed Kouider & Redouane Toumi, 2019. "Distributed multi-agent scheduling and control system for robotic flexible assembly cells," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1629-1644, April.
    17. García-Villoria, Alberto & Salhi, Said & Corominas, Albert & Pastor, Rafael, 2011. "Hyper-heuristic approaches for the response time variability problem," European Journal of Operational Research, Elsevier, vol. 211(1), pages 160-169, May.
    18. Gonzalo Mejía & Carlos Montoya, 2010. "Applications of resource assignment and scheduling with Petri Nets and heuristic search," Annals of Operations Research, Springer, vol. 181(1), pages 795-812, December.

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