An Artificial Bee Colony Algorithm for the Unrelated Parallel Machines Scheduling Problem

  • Francisco J. Rodriguez
  • Carlos García-Martínez
  • Christian Blum
  • Manuel Lozano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7492)


In this work, we tackle the problem of scheduling a set of jobs on a set of non-identical parallel machines with the goal of minimising the total weighted completion times. Artificial bee colony (ABC) algorithm is a new optimization technique inspired by the intelligent foraging behaviour of honey-bee swarm. These algorithms have shown a better or similar performance to those of other population-based algorithms, with the advantage of employing fewer control parameters. This paper proposes an ABC algorithm that combines the basic scheme with two significant elements: (1) a local search method to enhance the exploitation capability of basic ABC and (2) a neighbourhood operator based on iterated greedy constructive-destructive procedure. The benefits of the proposal in comparison to three different metaheuristic proposed in the literature are experimentally shown.


discrete optimisation metaheuristics artificial bee colony unrelated parallel machines schedulling problem 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Elmaghraby, S., Park, S.: Scheduling jobs on a number of identical machines. AIIE Transactions 6(1), 1–13 (1974)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Azizoglu, M., Kirca, O.: On the minimization of total weighted flow time with identical and uniform parallel machines. European Journal of Operational Research 113(1), 91–100 (1999)zbMATHCrossRefGoogle Scholar
  3. 3.
    Allahverdi, A., Gupta, J., Aldowaisan, T.: A review of scheduling research involving setup considerations. Omega 27(2), 219–239 (1999)CrossRefGoogle Scholar
  4. 4.
    Rosenbloom, E., Goertzen, N.: Cyclic nurse scheduling. European Journal of Operational Research 31, 19–23 (1987)CrossRefGoogle Scholar
  5. 5.
    Buxey, G.: Production scheduling: Practice and theory. European Journal of Operational Research 39, 17–31 (1989)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Dodin, B., Chan, K.H.: Application of production scheduling methods to external and internal audit scheduling. European Journal of Operational Research 52(3), 267–279 (1991)CrossRefGoogle Scholar
  7. 7.
    Pendharkar, P., Rodger, J.: Nonlinear programming and genetic search application for production scheduling in coal mines. Annals of Operations Research 95(1), 251–267 (2000)zbMATHCrossRefGoogle Scholar
  8. 8.
    Jarrah, A.I.Z., Bard, J.F., de Silva, A.H.: A heuristic for machine scheduling at general mail facilities. European Journal of Operational Research 63(2), 192–206 (1992)CrossRefGoogle Scholar
  9. 9.
    Rochat, Y.: A genetic approach for solving a scheduling problem in a robotized analytical system. Journal of Heuristics 4, 245–261 (1998)zbMATHCrossRefGoogle Scholar
  10. 10.
    Croce, F.D., Tadei, R., Asioli, P.: Scheduling a round robin tennis tournamentunder courts and players availability constraints. Annals of Operations Research 92, 349–361 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Brucker, P., Hurink, J.: Solving a chemical batch scheduling problem by local search. Annals of Operations Research 96(1), 17–38 (2000)zbMATHCrossRefGoogle Scholar
  12. 12.
    Azizoglu, M., Kirca, O.: Scheduling jobs on unrelated parallel machines to minimize regular total cost functions. IIE Transactions 31(2), 153–159 (1999)Google Scholar
  13. 13.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J. of Global Optimization 39(3), 459–471 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing 8(1), 687–697 (2008)CrossRefGoogle Scholar
  15. 15.
    Sundar, S., Singh, A.: A swarm intelligence approach to the quadratic minimum spanning tree problem. Information Sciences 180(17), 3182–3191 (2010)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Kashan, M.H., Nahavandi, N., Kashan, A.H.: DisABC: A new artificial bee colony algorithm for binary optimization. Applied Soft Computing 12(1), 342–352 (2012)CrossRefGoogle Scholar
  17. 17.
    Akbari, R., Hedayatzadeh, R., Ziarati, K., Hassanizadeh, B.: A multi-objective artificial bee colony algorithm. Swarm and Evolutionary Computation 2, 39–52 (2012)CrossRefGoogle Scholar
  18. 18.
    Jacobs, L., Brusco, M.: A local-search heuristic for large set-covering problems. Naval Research Logistics 42, 1129–1140 (1995)MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Ruiz, R., Stützle, T.: A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research 177(3), 2033–2049 (2007)zbMATHCrossRefGoogle Scholar
  20. 20.
    McNaughton, R.: Scheduling with deadlines and loss functions. Management Science 6(1), 1–12 (1959)MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Vredeveld, T., Hurkens, C.: Experimental comparison of approximation algorithms for scheduling unrelated parallel machines. Informs Journal on Computing 14(2), 175–189 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
    Lin, Y., Pfund, M., Fowler, J.: Heuristics for minimizing regular performance measures in unrelated parallel machine scheduling problems. Computers & Operations Research 38(6), 901–916 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  23. 23.
    Cheng, R., Gen, M., Tozawa, T.: Minmax earliness/tardiness scheduling in identical parallel machine system using genetic algorithms. Computers & Industrial Engineering 29(1-4), 513–517 (1995)CrossRefGoogle Scholar
  24. 24.
    Fanjul-Peyro, L., Ruiz, R.: Iterated greedy local search methods for unrelated parallel machine scheduling. European Journal of Operational Research 207(1), 55–69 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  25. 25.
    Zaidi, M., Jarboui, B., Loukil, T., Kacem, I.: Hybrid meta-heuristics for uniform parallel machine to minimize total weighted completion time. In: Proc. of 8th International Conference of Modeling and Simulation, MOSIM 2010 (2010)Google Scholar
  26. 26.
    Garcia, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: A case study on the CEC’2005 special session on real parameter optimization. Journal of Heuristics 15, 617–644 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Francisco J. Rodriguez
    • 1
  • Carlos García-Martínez
    • 3
  • Christian Blum
    • 2
  • Manuel Lozano
    • 1
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain
  2. 2.ALBCOM Research GroupTechnical University of CataloniaBarcelonaSpain
  3. 3.Department of Computing and Numerical AnalysisUniversity of CórdobaCórdobaSpain

Personalised recommendations