Skip to main content

A Scale-Free Based Memetic Algorithm for Resource-Constrained Project Scheduling Problems

  • Conference paper
Intelligent Data Engineering and Automated Learning – IDEAL 2013 (IDEAL 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8206))

Abstract

The resource-constrained project scheduling problem (RCPSP) is a popular problem that has attracted attentions of many researchers with various backgrounds. In this paper, a new memetic algorithm (MA) based on scale-free networks is proposed for solving RCPSPs, namely SFMA-RCPSPs. In SFMA, the chromosomes are located on a scale-free network. Thus, each chromosome can only communicate with the ones that have connections with it. In the experiments, benchmark problems, namely Patterson, J30 and J60, are used to validate the performance of SFMA. The results show that the SFMA performs well in finding out the best known solutions especially for Patterson and J30 data sets, besides, the average deviations from the best known solutions are small. Therefore, SFMA improves the search speed and effect.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Krzysztof, F., Khalil, S.H.: Solving the resource-constrained project scheduling problem by a variable neighbourhood search. European Journal of Operational Research 155(1), 402–413 (2004)

    MathSciNet  MATH  Google Scholar 

  2. Cheng, Z., Yi, Z.: Scale-free fully informed particle swarm optimization algorithm. Information Sciences 181(20), 4550–4568 (2011)

    Article  MathSciNet  Google Scholar 

  3. Oliver, H., Michael, S., Wolfgang, K.: Scale-Free Networks the Impact of Fat Tailed Degree Distribution on Diffusion and Communication Processes. Wirtschaftsinformatic, 267–275 (2006)

    Google Scholar 

  4. Barabási, A.L., Albert, R., Jeong, H.: Scale-free characteristics of random networks: the topology of the world wide web. Physica A 281(1-4), 69–77 (2000)

    Article  Google Scholar 

  5. Egúluz, V.M., Chialvo, D.R., Cencchi, G.A., Baliki, M., Apkarian, A.V.: Scale-free brain functional networks. Phys. Rev. Lett. 94(1) (2005)

    Google Scholar 

  6. Jeong, H., Mason, S., Barabási, A.L., Oltvai, Z.N.: Lethality and centrality in protein networks. Nature 411, 41–42 (2001)

    Article  Google Scholar 

  7. Khalil, S.H., Hongbo, Y., Krazysztof, F.: An Evolutionary Algorithm for Resource-Constrained Project Scheduling. IEEE Transactions on Evolutionary Computation 6(5), 512–518 (2002)

    Article  Google Scholar 

  8. Zhong, W., Liu, J., Xue, M., Jiao, L.: A multiagent genetic algorithm for global numerical optimization. IEEE Trans. on Syst., Man, and Cybern., Part B 34(2), 1128–1141 (2004)

    Article  Google Scholar 

  9. Liu, J., Zhong, W., Jiao, L.: A multiagent evolutionary algorithm for constraint satisfaction problems. IEEE Trans. on Syst., Man, and Cybern., Part B 36(1), 54–73 (2006)

    Article  Google Scholar 

  10. Liu, J., Zhong, W., Jiao, L.: Moving block sequence and organizational evolutionary algorithm for general floorplanning with arbitrarily shaped rectilinear blocks. IEEE Trans. on Evolutionary Computation 12(5), 630–646 (2008)

    Article  Google Scholar 

  11. Jiao, L., Liu, J., Zhong, W.: An organizational coevolutionary algorithm for classification. IEEE Trans. on Evolutionary Computation 10(1), 67–80 (2006)

    Article  Google Scholar 

  12. Liu, J., Zhong, W., Jiao, L.: A multiagent evolutionary algorithm for combinatorial optimization problems. IEEE Trans. on Systems, Man, and Cybernetics Part B 40(1), 229–240 (2010)

    Article  Google Scholar 

  13. Pablo, M.: On evolution, search, optimization, genetic algorithms and martial arts towards memetic algorithms Caltech concurrent computation program. C3P Report (1989)

    Google Scholar 

  14. Joshua, D. K., David, W.C.: M-PAES: A memetic algorithm for multiobjective optimization. Evolutionary Computation 1(1), 325–332 (2000)

    Google Scholar 

  15. Ong, Y.S., Keane, A.J.: Met-lamarckian learning in memetic algorithms. IEEE Transaction on Evolutionary Computation 8(2), 99–110 (2004)

    Article  Google Scholar 

  16. Ong, Y.S., Lim, M.H., Zhu, N.: Classification of adaptive memetic algorithms: a comparative study. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 36(1), 141–152 (2006)

    Article  Google Scholar 

  17. Ong, Y.S., Lim, M., Chen, X.: Memetic computation-Past, present & future (Research Frontier). IEEE Computational Intelligence Magazine 5(2), 24–31 (2010)

    Article  Google Scholar 

  18. Rainer, K., Sonke, H.: Experimental investigation of heuristics for resource-constrained project scheduling: An update. European Journal of Operational Research 174(1), 23–37 (2006)

    Article  MATH  Google Scholar 

  19. Tormos, P., Lova, A.: An efficient multi-pass heuristic for project scheduling with constrained resources. International Journal of Production Research 41, 1071–1086 (2003)

    Article  MATH  Google Scholar 

  20. Alcaraz, J., Maroto, C.: A robust genetic algorithm for resource allocation in project scheduling. Annals of Operations Research 102, 83–109 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  21. Valls, V., Ballestin, F., Quintanilla, M.S.: Justification and RCPSP: A technique that pays. European Journal of Operational Research 165, 375–386 (2005)

    Article  MATH  Google Scholar 

  22. Blazewicz, J., Lenstra, J.K., Rinnooy, A.H.G.: Scheduling subject to resource constraints: Classification and complexity. Discrete Appl. Maths. 5, 11–24 (1983)

    Article  MATH  Google Scholar 

  23. Valls, V., Ballestin, F., Quintanilla, M.S.: A hybrid genetic algorithm for the resource-constrained project scheduling problem. European Journal of Operational Research 185(2), 495–508 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  24. Tormos, P., Lova, A.: A Competitive Heuristic Solution Technique for Resource-Constrained Project Scheduling. Annals of Operations Research 102, 65–81 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  25. Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)

    Article  MathSciNet  Google Scholar 

  26. Kolisch, R., Sprecher, A., Drexl, A.: Characterization and generation of a general class of resource-constrained project scheduling problems. Management Science 41(10), 1693–1703 (1995)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wang, L., Liu, J. (2013). A Scale-Free Based Memetic Algorithm for Resource-Constrained Project Scheduling Problems. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-41278-3_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41277-6

  • Online ISBN: 978-3-642-41278-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics