SOLVING THE RCPSP WITH AN EVOLUTIONARY ALGORITHM BASED ON INSTANCE INFORMATION

José António Oliveira, Luís Dias, Guilherme Pereira

2012

Abstract

The Resource Constrained Project Scheduling Problem (RCPSP) is NP-hard thus justifying the use meta-heuristics for its solution. This paper presents an evolutionary algorithm developed for the RCPSP problem. This evolutionary algorithm uses an alphabet based on random keys that makes easier its implementation while solving combinatorial optimization problems. Random keys allow the use of conventional genetic operators, what makes easier the adaptation of the evolutionary algorithm to new problems. To improve the method's performance, this evolutionary algorithm uses an initial population that is generated considering the information available for the instance. This paper studies the impact of using that information in the initial population. The computational experiments presented compare two types of initial population - the conventional one (generated randomly) and this new approach that considers the information of the instance.

References

  1. Bean, J. C., 1994. Genetics and random keys for sequencing and optimization. ORSA Journal on Computing, 6, 154-160.
  2. Brucker, P., Knust, S., Schoo, A., Thiele, O., 1998. A branch and bound algorithm for the resourceconstrained project scheduling problem. European Journal of Operational Research, 107, 272-288.
  3. Cheng, R., Gen, M., Tsujimura, Y., 1996. A tutorial survey of job-shop scheduling problems using genetic algorithms part I: Representation. Computers & Industrial Engineering, 34 (4), 983-997.
  4. Demeulemeester, E., Vanhocke, M., Herroelen, W., 2003. RanGen: A Random Network Generator for Activityon-the-node Networks. Journal of Scheduling, 6, 17- 38.
  5. Giffler, B., Thompson, G. L., 1960. Algorithms for solving production scheduling problems. Operations Research, 8, 487-503.
  6. Gonçalves, J. F., Mendes, J. J., Resende, M. G. C., 2005. A hybrid genetic algorithm for the job shop scheduling problem. European Journal of Operational Research, 167, 77-95.
  7. Hartmann, S., Kolisch, R., 2000. Experimental evaluation of state-of-the-art heuristics for the resourceconstrained project scheduling problem. European Journal of Operational Research, 127, 2, 394-407.
  8. Kolisch, R., 1996. Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation. European Journal of Operational Research, 90, 320-333.
  9. Kolisch, R., Hartmann S., 1999. Heuristic algorithms for solving the resource-constrained project scheduling problem - Classification and computational analysis, in Weglarz, J. (Eds.): Project Scheduling - Recent Models, Algorithms and Applications, Kluwer, Boston, p. 147 - 178.
  10. Kolisch, R., Hartmann, S., 2006. Experimental investigation of heuristics for resource-constrained project scheduling: An update. European Journal of Operational Research, 174, 1, 23-37.
  11. Kolisch, R., Sprecher, A., 1997. PSPLIB - A project scheduling library. European Journal of Operational Research, 96, 205-216.
  12. Lian, Z., Gu, X., Jiao, B., 2006. A similar particle swarm optimization algorithm for job-shop scheduling to minimize makespan. Applied Mathematics and Computation, 183, 1008-1017.
  13. Liu, M., Hao, J., Wu, C., 2008. A prediction based iterative decomposition algorithm for scheduling large-scale job shops, Mathematical and Computer Modelling, 47, 411-421.
  14. Oliveira, J. A., Dias, L., Pereira, G., 2010. Solving the Job Shop Problem with a random keys genetic algorithm with instance parameters. Proceedings of 2nd International Conference on Engineering Optimization - EngOpt 2010, Lisbon - Portugal.
  15. Ranjbar, M., Kianfar, F., 2009. A Hybrid Scatter Search for the RCPSP. Transaction E: Industrial Engineering, 16, 11-18.
  16. Silva, H., Oliveira, J. A., Tereso, A., 2010. Um Algoritmo Genético para Programação de Projectos em Redes de Actividades com Complementaridade de Recursos. Revista Ibérica de Sistemas y Tecnologías de la Información,. 6, 59-72.
  17. Sprecher, A., Kolisch, R., Drexl A., 1995. Semi-active, active, and non-delay schedules for the resourceconstrained project scheduling problem, European Journal of Operational Research, 80, 94-102.
  18. Vaessens, R., Aarts, E., Lenstra, J. K., 1996. Job Shop Scheduling by local search. INFORMS Journal on Computing, 8, 302-317.
  19. Zhang, C., Li, P., Guan, Z., Rao, Y., 2007. A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem. Computers & Operations Research, 53, 313-320.
Download


Paper Citation


in Harvard Style

António Oliveira J., Dias L. and Pereira G. (2012). SOLVING THE RCPSP WITH AN EVOLUTIONARY ALGORITHM BASED ON INSTANCE INFORMATION . In Proceedings of the 1st International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-8425-97-3, pages 157-164. DOI: 10.5220/0003759401570164


in Bibtex Style

@conference{icores12,
author={José António Oliveira and Luís Dias and Guilherme Pereira},
title={SOLVING THE RCPSP WITH AN EVOLUTIONARY ALGORITHM BASED ON INSTANCE INFORMATION},
booktitle={Proceedings of the 1st International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2012},
pages={157-164},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003759401570164},
isbn={978-989-8425-97-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - SOLVING THE RCPSP WITH AN EVOLUTIONARY ALGORITHM BASED ON INSTANCE INFORMATION
SN - 978-989-8425-97-3
AU - António Oliveira J.
AU - Dias L.
AU - Pereira G.
PY - 2012
SP - 157
EP - 164
DO - 10.5220/0003759401570164