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A Differential Evolution Algorithm for Solving Resource Constrained Project Scheduling Problems

  • Ismail M. AliEmail author
  • Saber Mohammed Elsayed
  • Tapabrata Ray
  • Ruhul A. Sarker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9592)

Abstract

The resource constrained project scheduling problem is considered as a complex scheduling problem. In order to solve this NP-hard problem, an efficient differential evolution (DE) algorithm is proposed in this paper. In the algorithm, improved mutation and crossover operators are introduced with an aim to maintain feasibility for generated individuals and hence being able to converge quickly to the optimal solutions. The algorithm is tested on a set of well-known project scheduling problem library (PSPLIB), with instances of 30, 60, 90 and 120 activities. The proposed DE is shown to have superior performance in terms of lower average deviations from the optimal solutions compared to some of the state-of-the-art algorithms.

Keywords

Differential Evolution Differential Evolution Algorithm Mutant Vector Resource Constrain Project Schedule Problem Resource Constrain Project Schedule Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Zhai, X., Tiong, R.L.K., Bjornsson, H.C., Chua, D.K.H.: A simulation-GA based model for production planning in precast plant. In: Proceedings of the 38th Conference on Winter Simulation, pp. 1796–1803. Winter Simulation Conference (2006)Google Scholar
  2. 2.
    Mathaisel, D.F., Comm, C.L.: Course and classroom scheduling: an interactive computer graphics approach. J. Syst. Softw. 15, 149–157 (1991)CrossRefGoogle Scholar
  3. 3.
    Chang, S.C.: A new aircrew-scheduling model for short-haul routes. J. Air Transp. Manag. 8, 249–260 (2002)CrossRefGoogle Scholar
  4. 4.
    Fleming, P.J., Fonseca, C.M.: Genetic algorithms in control systems engineering: a brief introduction. In: IEE Colloquium on Genetic Algorithms for Control Systems Engineering, pp. 1/1–1/5. IET (1993)Google Scholar
  5. 5.
    Brucker, P., Drexl, A., Möhring, R., Neumann, K., Pesch, E.: Resource-constrained project scheduling: notation, classification, models, and methods. Eur. J. Oper. Res. 112, 3–41 (1999)CrossRefzbMATHGoogle Scholar
  6. 6.
    Kolisch, R., Padman, R.: An integrated survey of deterministic project scheduling. Omega 29, 249–272 (2001)CrossRefGoogle Scholar
  7. 7.
    Hartmann, S., Briskorn, D.: A survey of variants and extensions of the resource-constrained project scheduling problem. Eur. J. Oper. Res. 207, 1–14 (2010)CrossRefMathSciNetzbMATHGoogle Scholar
  8. 8.
    Demeulemeester, E., Herroelen, W.: A branch-and-bound procedure for the multiple resource-constrained project scheduling problem. Manag. Sci. 38, 1803–1818 (1992)CrossRefzbMATHGoogle Scholar
  9. 9.
    Li, C., Bettati, R., Zhao, W.: Static priority scheduling for ATM networks. In: Proceedings of the 18th IEEE Real-Time Systems Symposium, pp. 264–273. IEEE (1997)Google Scholar
  10. 10.
    Lupetti, S., Zagorodnov, D.: Data popularity and shortest-job-first scheduling of network transfers. In: ICDT 2006 International Conference on Digital Telecommunications, pp. 26–26. IEEE (2006)Google Scholar
  11. 11.
    Cheng, X., Wu, C.: Hybrid algorithm for complex project scheduling. Comput. Integr. Manuf. Syst. Beijing 12, 585 (2006)Google Scholar
  12. 12.
    Zheng, X.-L., Wang, L.: A multi-agent optimization algorithm for resource constrained project scheduling problem. Expert Syst. Appl. 42, 6039–6049 (2015)CrossRefGoogle Scholar
  13. 13.
    Nonobe, K., Ibaraki, T.: Formulation and tabu search algorithm for the resource constrained project scheduling problem. Essays and Surveys in Metaheuristics, pp. 557–588. Springer, Berlin (2002)CrossRefGoogle Scholar
  14. 14.
    Fang, C., Wang, L.: An effective shuffled frog-leaping algorithm for resource-constrained project scheduling problem. Comput. Oper. Res. 39, 890–901 (2012)CrossRefMathSciNetzbMATHGoogle Scholar
  15. 15.
    Chen, W., Ni, X.: Chaotic differential evolution algorithm for resource constrained project scheduling problem. Int. J. Comput. Sci. Math. 5, 81–93 (2014)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Damak, N., Jarboui, B., Siarry, P., Loukil, T.: Differential evolution for solving multi-mode resource-constrained project scheduling problems. Comput. Oper. Res. 36, 2653–2659 (2009)CrossRefMathSciNetzbMATHGoogle Scholar
  17. 17.
    Cheng, M.Y., Tran, D.H.: An efficient hybrid differential evolution based serial method for multimode resource-constrained project scheduling. KSCE J. Civil Eng. 1–11 (2015)Google Scholar
  18. 18.
    Jia, D., Zheng, G., Khan, M.K.: An effective memetic differential evolution algorithm based on chaotic local search. Inf. Sci. 181, 3175–3187 (2011)CrossRefGoogle Scholar
  19. 19.
    Christofides, N., Alvarez-Valdés, R., Tamarit, J.M.: Project scheduling with resource constraints: a branch and bound approach. Eur. J. Oper. Res. 29, 262–273 (1987)CrossRefzbMATHGoogle Scholar
  20. 20.
    Kolisch, R., Hartmann, S.: Heuristic algorithms for the resource-constrained project scheduling problem: classification and computational analysis. In: Weglarz, J. (ed.) Project Scheduling, vol. 14, pp. 147–178. Springer, Berlin (1999)CrossRefGoogle Scholar
  21. 21.
    Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)CrossRefMathSciNetzbMATHGoogle Scholar
  22. 22.
    Blum, C., Chiong, R., Clerc, M., De Jong, K., Michalewicz, Z., Neri, F., Weise, T.: Evolutionary Optimization. Variants of Evolutionary Algorithms for Real-World Applications, pp. 1–29. Springer, Berlin (2012)Google Scholar
  23. 23.
    Kolisch, R., Schwindt, C., Sprecher, A.: Benchmark instances for project scheduling problems. In: Weglarz, J. (ed.) Project Scheduling, vol. 14, pp. 197–212. Springer, Berlin (1999)CrossRefGoogle Scholar
  24. 24.
    Stinson, J.P., Davis, E.W., Khumawala, B.M.: Multiple resource–constrained scheduling using branch and bound. AIIE Trans. 10, 252–259 (1978)CrossRefGoogle Scholar
  25. 25.
    Ronkkonen, J., Kukkonen, S., Price, K.V.: Real-parameter optimization with differential evolution. In: Proceedings of IEEE CEC, Vol. 1, pp. 506–513 (2009)Google Scholar
  26. 26.
    Fahmy, A., Hassan, T.M., Bassioni, H.: Improving RCPSP solutions quality with stacking justification-application with particle swarm optimization. Expert Syst. Appl. 41, 5870–5881 (2014)CrossRefGoogle Scholar
  27. 27.
    Zamani, R.: A competitive magnet-based genetic algorithm for solving the resource-constrained project scheduling problem. Eur. J. Oper. Res. 229, 552–559 (2013)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ismail M. Ali
    • 1
    Email author
  • Saber Mohammed Elsayed
    • 1
  • Tapabrata Ray
    • 1
  • Ruhul A. Sarker
    • 1
  1. 1.School of Engineering and Information TechnologyUniversity of New South WalesCanberraAustralia

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