Abstract
The paper proposes an implementation of the population learning algorithm (PLA) for solving the permutation flowshop scheduling problem (PFSP). The PLA can be considered as a useful framework for constructing a hybrid approaches. In the proposed implementation the PLA scheme is used to integrate evolutionary, tabu search and simulated annealing algorithms. The approach has been evaluated experimentally. Experiment has produced 14 new upper bounds for the standard benchmark dataset containing 120 PFSP instances and has shown that the approach is competitive to other algorithms.
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References
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Jędrzejowicz, J., Jędrzejowicz, P.: PLA-Based Permutation Scheduling. Foundations of Computing and Decision Sciences 28(3), 159–177 (2003)
Ruiz, R., Maroto, C., Alcaraz, J.: New Genetic Algorithms for the Permutation Flowshop Scheduling Problems. In: Proc. The Fifth Metaheuristic International Conference, Kyoto, 63-1–63-8 (2003)
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© 2005 Springer-Verlag Berlin Heidelberg
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Jędrzejowicz, J., Jędrzejowicz, P. (2005). New Upper Bounds for the Permutation Flowshop Scheduling Problem. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_33
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DOI: https://doi.org/10.1007/11504894_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26551-1
Online ISBN: 978-3-540-31893-4
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