Hybrid Bacterial Iterated Greedy heuristics for the Permutation Flow Shop Problem

2012 IEEE Congress on Evolutionary Computation, CEC 2012

Volumn , Issue , 2012, Pages

  Balázs, Krisztián ^a   Horváth, Zoltán ^b   Kóczy, László T ^b  

^a BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS   (Hungary)

^b SZÉCHENYI ISTVÁN UNIVERSITY   (Hungary)

Author keywords

Bacterial methods; Combinatorial optimization; Hybrid Iterated Greedy techniques; Memetic algorithms; Permutation Flow Shop Problem

Indexed keywords

BACTERIAL METHODS; BENCH-MARK PROBLEMS; GREEDY HEURISTICS; HYBRID ITERATED GREEDY TECHNIQUES; HYBRID METHOD; MEMETIC ALGORITHMS; PERMUTATION FLOW SHOPS; STATE-OF-THE-ART METHODS;

COMBINATORIAL OPTIMIZATION;

EVOLUTIONARY ALGORITHMS;

EID: 84866870408     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2012.6256167     Document Type: Conference Paper

References (14)

1. S. M. Johnson, Optimal two- and three-stage production schedules with setup times included, Naval Research Logistics Quarterly 1, 1954, pp. 61-68.
2. A. H. G. Rinnooy Kan: Machine Scheduling Problems: Classification, Complexity and Computations, Martinus Nijhoff, The Hague, The Netherlands, 1976.
3. E. Taillard: Some efficient heuristic methods for the flow shop sequencing problem, European Journal of Operational Research, Amsterdam, 1990, pp. 65-74.
4. A. Juan, A. Guix, R. Ruiz, P. Fonseca, F. Adelantado: Using simulation to provide alternative solutions to the flowshop sequencing problem, 14th ASIM Dedicated Conf. on Simulation in Production and Logistic, Karlsruhe, Germany, 2010, pp. 349-356.
5. Z. Horváth, P. Pusztai, T. Hajba, Ch. Kiss-Tóth: Mathematical Methods and Parallel Codes for Production Line Optimization, Factory Automation 2011 Conference, Gyo{combining double acute accent}r, Hungary, 2011.
6. E. Taillard, Benchmarks for basic scheduling problems, European Journal of Operational Research 64 (2), 1993, pp. 278-285.
7. K. Balázs, Z. Horváth and L. T. Kóczy: Different Chromosome Based Evolutionary Approaches for the Permutation Flow Shop Problem, Acta Polytechnica Hungarica, in press.
8. R. Ruiz, T. Stützle: A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem, European Journal of Operational Research, 177, 2007, pp. 2033-2049.
9. J. H. Holland: Adaption in Natural and Artificial Systems, The MIT Press, Cambridge, Massachusetts, 1992.
10. N. E. Nawa and T. Furuhashi: Fuzzy system parameters discovery by bacterial evolutionary algorithm, IEEE Transactions on Fuzzy Systems, 7(5), 1999, pp. 608-616.
11. K. Balázs, J. Botzheim, L. T. Kóczy: Comparison of Various Evolutionary and Memetic Algorithms, Proceedings of the International Symposium on Integrated Uncertainty Management and Applications, IUM 2010, Ishikawa, Japan, 2010, pp. 431-442.
12. K. Balázs, J. Botzheim, L. T. Kóczy: Comparative Analysis of Interpolative and Non-interpolative Fuzzy Rule Based Machine Learning Systems Applying Various Numerical Optimization Methods, World Congress on Computational Intelligence, WCCI 2010, Barcelona, Spain, 2010, pp. 875-982.
13. P. Moscato: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms, Technical Report Caltech Concurrent Computation Program, Report. 826, California Institute of Technology, Pasadena, USA, 1989.
14. M. Nawaz, E. E. Enscore Jr. and I. Ham: A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem, OMEGA, The International Journal of Management Science 11 (1), 1983, pp. 91-95.