HYBRID PARAMETER-LESS EVOLUTIONARY ALGORITHM IN PRODUCTION PLANNING

Vida Vukašinovič, Peter Korošec, Gregor Papa

2010

Abstract

In the real-world production planning problems there are many constraints that need to be considered. Usually, these constraints and interdependent and the optimization algorithms has to efficiently solve that. This paper presents the hybrid parameter-less evolutionary algorithm used for construction of an optimal production plan. The algorithm is based on genetic algorithm, but is modified to work without the parameter setting. All algorithm control parameters are automatically determined during the optimization. The algorithm was able to solve the constraints and to make an optimal production plan. Additionally, we evaluated the influence of different ratios of orders with fixed deadlines on the performance of the algorithm. The used algorithm can successfully solve also these additional constraints.

References

  1. Bäck, T. (1996). Evolutionary Algorithms in Theory and Practice. Oxford University Press.
  2. Brest, J., Mernik, S. G. B. B. M., and Z?umer, V. (2006). Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation, 10(6):646-657.
  3. Brucker, P. (1998). Scheduling algorithms. Springer, Heidelberg, 2nd edition.
  4. Caumond, A., Lacomme, P., and Tchernev, N. (2008). A memetic algorithm for the job-shop with time-lags. Comput. Oper. Res., 35(7):2331-2356.
  5. Chryssolouris, G. and Subramaniam, V. (2001). Dynamic scheduling of manufacturing job shops using genetic algorithms. Journal of Intelligent Manufacturing, 12(3):281-293.
  6. Goldberg, D. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley.
  7. Harik, G. and Lobo, F. (1999). A parameter-less genetic algorithm. In Proc. Genetic and Evolutionary Computation Conference (GECCO 1999), pages 258-265.
  8. Hasan, S. M. K., Sarker, R., Essam, D., and Cornforth, D. (2009). Memetic algorithms for solving job-shop scheduling problems. Memetic Computing, 1(1):69- 83.
  9. Koros?ec, P., Papa, G., and Vukas?inovic, V. (2010). Application of memetic algorithm in production planning. In Bioinspired Optimization Methods and their Applications, pages 163-175.
  10. Ong, Y. and Keane, A. (2004). Meta-lamarckian learning in memetic algorithms. IEEE Transactions on Evolutionary Computation, 8(2):99-110.
  11. Papa, G. (2008). Parameter-less evolutionary search. In Proc. Genetic and Evolutionary Computation Conference (GECCO'08), pages 1133-1134.
  12. Senthilkumar, P. and Shahabudeen, P. (2006). Ga based heuristic for the open job shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 30(3-4):297-301.
  13. Vazquez, M. and Whitley, L. D. (2000). A comparison of genetic algorithms for the static job shop scheduling problem. In Parallel Problem Solving from Nature, pages 303-312. Springer.
Download


Paper Citation


in Harvard Style

Vukašinovič V., Korošec P. and Papa G. (2010). HYBRID PARAMETER-LESS EVOLUTIONARY ALGORITHM IN PRODUCTION PLANNING . In Proceedings of the International Conference on Evolutionary Computation - Volume 1: ICEC, (IJCCI 2010) ISBN 978-989-8425-31-7, pages 231-236. DOI: 10.5220/0003085002310236


in Bibtex Style

@conference{icec10,
author={Vida Vukašinovič and Peter Korošec and Gregor Papa},
title={HYBRID PARAMETER-LESS EVOLUTIONARY ALGORITHM IN PRODUCTION PLANNING},
booktitle={Proceedings of the International Conference on Evolutionary Computation - Volume 1: ICEC, (IJCCI 2010)},
year={2010},
pages={231-236},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003085002310236},
isbn={978-989-8425-31-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation - Volume 1: ICEC, (IJCCI 2010)
TI - HYBRID PARAMETER-LESS EVOLUTIONARY ALGORITHM IN PRODUCTION PLANNING
SN - 978-989-8425-31-7
AU - Vukašinovič V.
AU - Korošec P.
AU - Papa G.
PY - 2010
SP - 231
EP - 236
DO - 10.5220/0003085002310236