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Using Firefly Algorithm to Solve Resource Constrained Project Scheduling Problem

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 201))

Abstract

The Firefly Algorithm (FA) is among the most recently introduced meta-heuristics. This work aims at study the application of FA algorithm to solve the Resource Constrained Project Scheduling Problem (RCPSP). The algorithm starts by generating a set of random schedules. After that, the initial schedules are improved iteratively using the flying approach proposed by the FA. By termination of algorithm, the best schedule found by the method is returned as the final result. The results of the state-of-art algorithms are used in this work in order to evaluate the performance of the proposed method. The comparison study shows the efficiency of the proposed method in solving RCPSP. The proposed method has competitive performance compared to the other RCPSP solvers.

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References

  • S. Hartmann, R. Kolisch, Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem, European Journal of Operational Research 127 (2000) 394–407.

    Google Scholar 

  • R. Kolisch, S. Hartmann, Experimental investigation of heuristics for resource-constrained project scheduling: an update, European Journal of Operational Research 174 (2006) 23–37.

    Google Scholar 

  • R. Kolisch, S. Hartmann, Heuristic algorithms for solving the resource-constrained project scheduling problem: classification and computational analysis, in: J. Weglarz (Ed.), Project Scheduling: Recent Models, algorithms and Applications, Kluwer Academic Publishers, Berlin, 1999, pp. 147–178.

    Google Scholar 

  • R. Kolisch, R. Padman, An integrated survey of deterministic project scheduling, Omega 29 (2001) 249–272.

    Google Scholar 

  • Baar T, Brucker P, Knust S. Tabu-search algorithms and lower bounds for the resource-constrained project scheduling problem. In: Voss S, Martello S, Osman I, Roucairol C, editors. Meta-heurisitics: advances and trends in local search paradigms for optimization. Dordrecht: Kluwer; 1998. p. 1–8.

    Google Scholar 

  • K. Bouleimen and H. Lecocq. “A new efficient simulated annealing algorithm for the resource– constrained project scheduling problem and its multiple modes version”. European Journal of Operational Research, 149, (2003) 268–281.

    Google Scholar 

  • S. Hartmann, A competitive genetic algorithm for resource-constrained project scheduling, Naval Research Logistics 45 (1998) 733–750.

    Google Scholar 

  • S. Hartmann, A self-adapting genetic algorithm for project scheduling under resource constraints, Naval Research Logistics 49 (2002) 433–448.

    Google Scholar 

  • J.J.M. Mendes, J.F. Goncalves, M.G.C. Resende, A random key based genetic algorithm for the resource constrained project scheduling problem, Computers & Operations Research 36 (2009) 92–109.

    Google Scholar 

  • M. Ranjbar, F. Kianfar, S. Shadrokh, Solving the resource availability cost problem in project scheduling by path relinking and genetic algorithm, Applied Mathematics and Computation 196 (2008) 879–888.

    Google Scholar 

  • D. Merkle, M. Middendorf, H. Schmeck, Ant colony optimization for resource-constrained project scheduling, IEEE Transactions on Evolutionary Computation 6 (2002) 333–346.

    Google Scholar 

  • B. Jarboui, N. Damak, P. Siarry, A. Rebai, A combinatorial particle swarm optimization for solving multi-mode resource-constrained project schedulingproblems, Applied Mathematics and Computation 195 (2008) 299–308.

    Google Scholar 

  • Luo, X., Wang, D., Tang, J., & Tu, Y. (2006). An improved PSO algorithm for resourceconstrainedproject scheduling problem, intelligent control and automation,2006. In The sixth world congress on WCICA 2006 (Vol. 1, pp. 3514–3518).

    Google Scholar 

  • Zhang, C., Sun, J., Zhu, X., & Yang, Q. (2008). An improved particle swarmoptimization algorithm for flowshop scheduling problem. InformationProcessing Letters, 108(4), 204–209.

    Google Scholar 

  • K. Ziarati, R. Akbari, and V. Zeighami, “On the Performance of Bee Algorithms for Resource Constrained Project Scheduling Problem”, Journal of Applied Soft Computing, Elsevier, Vol.11, No. 4, pp. 3720-3733, 2011.

    Google Scholar 

  • Akbari R., Zeighami V., and Ziarati K., Artificial Bee colony for resource constrained project scheduling problem, International Journal of Industrial Engineering Computations, DOI: 10.5267/j.ijiec.2010.04.004.

  • Tseng, L.Y., & Chen, S. C.(2006). A hybrid metaheuristic for the resource-constrained project scheduling problem, European Journal of Operational Research, 175, 707–721.

    Google Scholar 

  • Chen, W., Shi, Y.J., Teng, H.F., Lan, X. P., Hu, L. C.(2010). An efficient hybrid algorithm for resource-constrained project scheduling. Information Sciences, 180, 1031–1039.

    Google Scholar 

  • Agarwal, A., Colak, S., &Erenguc, S.(2010). A Neurogenetic approach for the resource-constrained project scheduling problem. Computers & Operations Research doi:10.1016/j.cor.2010.01.007.

  • Valls V., Ballestın F., & Quintanilla, S.(2008). A hybrid genetic algorithm for the resource-constrained project scheduling problem. European Journal of Operational Research, 185, 495–508.

    Google Scholar 

  • X.-S. Yang, “Firefly Algorithms for multimodal optimization”, Lecture Notes in Computer Science, vol. 5792, pp. 169-178, 2009.

    Google Scholar 

  • Kolisch R. Serial and parallel resource-constrained project scheduling methods revisite: theory and computation. European Journal of Operational Research 1996;90:320–33.

    Google Scholar 

  • Hong Zhang, Xiaodong Li, Heng Li, Fulai Huang, “Particle swarm optimization-based schemes for resource-constrained project scheduling”, in Journal of Automation in Construction, Vol. 14 (2005) 393– 404.

    Google Scholar 

  • Project Scheduling Problem Library – PSPLIB: <http://129.187.106.231/psplib/>.

  • D. Debels, B. De Reyck, R. Leus, and M. Vanhoucke. “A hybrid scatter search/Electromagnetism meta–heuristic for project scheduling”. European Journal of Operational Research, 2004. To appear.

    Google Scholar 

  • J. Alcaraz, C. Maroto, and R. Ruiz. “Improving the performance of genetic algorithms for the RCPS problem”. Proceedings of the Ninth International Workshop on Project Management and Scheduling, (2004) pages 40–43, Nancy,.

    Google Scholar 

  • Ruey-Maw Chen “Particle swarm optimization with justification and designed mechanisms for resource-constrained project scheduling problem” Expert Systems with Applications, Volume 38, Issue 6, June 2011, Pages 7102-7111.

    Google Scholar 

  • Tormos P, Lova A. Integrating heuristics for resource constrained project scheduling: one step forward. Technical Report, Department ofStatistics and Operations Research, Universidad Politecnica de Valencia; 2003.

    Google Scholar 

  • Nonobe K, Ibaraki T. Formulation and tabu search algorithm for the resource constrained project scheduling problem. In: Ribeiro CC, Hansen P, editors. Essays and surveys in metaheuristics. Dordrecht: Kluwer Academic Publishers; 2002. p. 557–588.

    Google Scholar 

  • Hartmann S. A competitive genetic algorithm for resource-constrained project scheduling. Naval Research Logistics 1998;45:279–302.

    Google Scholar 

  • J. Coelho and L. Tavares. “Comparative analysis of meta–heuricstics for the resource constrained project scheduling problem”. Technical report, Department of Civil Engineering, Instituto Superior Tecnico, Portugal, 2003.

    Google Scholar 

  • A. Schirmer. “Case–based reasoning and improved adaptive search for project scheduling”. Naval Research Logistics, 47 (2000) 201–222.

    Google Scholar 

  • R. Kolisch and A. Drexl. “Adaptive search for solving hard project scheduling problems”. Naval Research Logistics, 43 (1996) 23–40.

    Google Scholar 

  • Kolisch R. Project scheduling under resource constraints: efficient heuristics for several problem classes. Wurzburg: Physica-Verlag; 1995.

    Google Scholar 

  • Leon VJ, Ramamoorthy B. Strength and adaptability of problem-space based neighborhoods for resource constrained scheduling. Operations Research Spektrum 1995;17:173–82.

    Google Scholar 

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

    Google Scholar 

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Correspondence to Pejman Sanaei .

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© 2013 Springer India

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Sanaei, P., Akbari, R., Zeighami, V., Shams, S. (2013). Using Firefly Algorithm to Solve Resource Constrained Project Scheduling Problem. In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Advances in Intelligent Systems and Computing, vol 201. Springer, India. https://doi.org/10.1007/978-81-322-1038-2_35

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  • DOI: https://doi.org/10.1007/978-81-322-1038-2_35

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  • Publisher Name: Springer, India

  • Print ISBN: 978-81-322-1037-5

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