Chromosome Mutation vs. Gene Mutation in Evolutive Approaches for Solving the Resource-Constrained Project Scheduling Problem (RCPSP)

  • Daniel Morillo
  • Federico Barber
  • Miguel A. Salido
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10868)


Resource-Constrained Project Scheduling Problems (RCPSP) are some of the most important scheduling problems due to their applicability to real problems and their combinatorial complexity (NP-hard). In the literature, it has been shown that metaheuristic algorithms are the main option to deal with real-size problems. Among them, population-based algorithms, especially genetic algorithms, stand out for being able to achieve the best near-optimal solutions in reasonable computational time. One of the main components of metaheuristic algorithms is the solution representation (codification) since all search strategies are implemented based on it. However, most codings are affected by generating redundant solutions, which obstruct incorporating new information. In this paper, we focus on the study of the mutation operator (responsible for diversity in the population), in order to determine how to implement this operator to reduce the obtaining of redundant solutions. The computational assessment was done on the well-known PSPLIB library and shows that the proposed algorithm reaches competitive solutions compared with the best-proposed algorithms in the literature.


RCPSP Redundant solutions Mutation operator 



This paper has been partially supported by the Spanish research projects TIN-2013-46511-C2-1-P and TIN2016-80856-R.


  1. 1.
    Blazewicz, J., Lenstra, J., Kan, A.: Scheduling subject to resource constraints: classification and complexity. Discrete Appl. Math. 5(1), 11–24 (1983)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Boctor, F.F.: Resource-constrained project scheduling by simulated annealing. Int. J. Prod. Res. 34(8), 2335–2351 (1996)CrossRefGoogle Scholar
  3. 3.
    Bouleimen, K., Lecocq, H.: A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version. Eur. J. Oper. Res. 149(2), 268–281 (2003)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Chen, R.M.: Particle swarm optimization with justification and designed mechanisms for resource-constrained project scheduling problem. Expert Syst. Appl. 38(6), 7102–7111 (2011)CrossRefGoogle Scholar
  5. 5.
    Chen, W., Shi, Y.J., Teng, H.F., Lan, X.P., Hu, L.C.: An efficient hybrid algorithm for resource-constrained project scheduling. Inf. Sci. 180(6), 1031–1039 (2010)CrossRefGoogle Scholar
  6. 6.
    Debels, D., De Reyck, B., Leus, R., Vanhoucke, M.: A hybrid scatter search/electromagnetism meta-heuristic for project scheduling. Eur. J. Oper. Res. 169(2), 638–653 (2006)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Debels, D., Vanhoucke, M.: A decomposition-based genetic algorithm for the resource-constrained project-scheduling problem. Oper. Res. 55(3), 457–469 (2007)CrossRefGoogle Scholar
  8. 8.
    Fahmy, A., Hassan, T.M., Bassioni, H.: Improving RCPSP solutions quality with stacking justification - application with particle swarm optimization. Expert Syst. Appl. 41(13), 5870–5881 (2014)CrossRefGoogle Scholar
  9. 9.
    Hartmann, S.: A self-adapting genetic algorithm for project scheduling under resource constraints. Naval Res. Logist. 49(5), 433–448 (2002)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Herroelen, W., De Reyck, B., Demeulemeester, E.: Resource-constrained project scheduling: a survey of recent developments. Comput. Oper. Res. 25(4), 279–302 (1998)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Klein, R.: Bidirectional planning: improving priority rule-based heuristics for scheduling resource-constrained projects. Eur. J. Oper. Res. 127(3), 619–638 (2000)CrossRefGoogle Scholar
  12. 12.
    Kochetov, Y.A., Stolyar, A.A.: Evolutionary local search with variable neighborhood for the resource constrained project scheduling problem. In: Workshop on Computer Science and Information Technologies CSIT 2003, Ufa, Russia (2003)Google Scholar
  13. 13.
    Kolisch, R., Hartmann, S.: Experimental investigation of heuristics for resource-constrained project scheduling: an update. Eur. J. Oper. Res. 174(1), 23–37 (2006)CrossRefGoogle Scholar
  14. 14.
    Kolisch, R., Sprecher, A.: PSPLIB - a project scheduling library. Eur. J. Oper. Res. 96, 205–216 (1996)CrossRefGoogle Scholar
  15. 15.
    Mahdi Mobini, M.D., Rabbani, M., Amalnik, M.S., Razmi, J., Rahimi-Vahed, A.R.: Using an enhanced scatter search algorithm for a resource-constrained project scheduling problem. Soft. Comput. 13(6), 597–610 (2008)CrossRefGoogle Scholar
  16. 16.
    Mendes, J., Gonçalves, J., Resende, M.: A random key based genetic algorithm for the resource constrained project scheduling problem. Comput. Oper. Res. 36(1), 92–109 (2009)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Nonobe, K., Baraki, T.: Formulation and Tabu search algorithm for the resource constrained project scheduling problem. In: Essays and Surveys in Metaheuristics, pp. 557–588. Springer, Boston (2002).
  18. 18.
    Paraskevopoulos, D., Tarantilis, C., Ioannou, G.: Solving project scheduling problems with resource constraints via an event list-based evolutionary algorithm. Expert Syst. Appl. 39(4), 3983–3994 (2012)CrossRefGoogle Scholar
  19. 19.
    Peteghem, V.V., Vanhoucke, M.: A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem. Eur. J. Oper. Res. 201(2), 409–418 (2010)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Tormos, P., Lova, A.: A competitive heuristic solution technique for resource-constrained project scheduling. Ann. Oper. Res. 102(1–4), 65–81 (2001)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Tseng, L.Y., Chen, S.C.: A hybrid metaheuristic for the resource-constrained project scheduling problem. Eur. J. Oper. Res. 175(2), 707–721 (2006)CrossRefGoogle Scholar
  22. 22.
    Valls, V., Ballestin, F., Quintanilla, S.: A hybrid genetic algorithm for the resource-constrained project scheduling problem. Eur. J. Oper. Res. 185(2), 495–508 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Daniel Morillo
    • 1
  • Federico Barber
    • 2
  • Miguel A. Salido
    • 2
  1. 1.Departamento de Ingeniería Civil e IndustrialPontificia Universidad Javeriana CaliCaliColombia
  2. 2.Instituto de Automática e Informática IndustrialUniversitat Politècnica de ValènciaValènciaSpain

Personalised recommendations