Scheduling the Flowshop with Zero Intermediate Storage Using Chaotic Discrete Artificial Bee Algorithm

  • Magdalena Metlická
  • Donald Davendra
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 289)


This paper analyses the application of the Chaos driven Discrete Artificial Bee Algorithm to the flowshop with zero intermediate storage problem. Nine unique chaos maps are embedded in the Discrete Artificial Bee Algorithm alongside the Mersenne twister and evaluated on the Taillard problem sets for the total flowtime criterion. Based on the obtained results and statistical analysis, it is shown that a number of chaos driven algorithms significantly performed better than the Mersenne Twister variant.


Artificial Bee Colony Flowshop with zero intermediate storage Chaos Maps 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alatas, B., Akin, E., Ozer, A.: Chaos embedded particle swarm optimization algorithms. Chaos, Solitons and Fractals 40(4), 1715–1734 (2009)CrossRefMATHMathSciNetGoogle Scholar
  2. 2.
    Caponetto, R., Fortuna, L., Fazzino, S., Xibilia, M.: Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Transactions on Evolutionary Computation 7(3), 289–304 (2003)CrossRefGoogle Scholar
  3. 3.
    Chang, J.H., Chiu, H.N.: A comprehensive review of lot streaming. International Journal of Production Research 43(8), 1515–1536 (2005)CrossRefGoogle Scholar
  4. 4.
    Davendra, D., Zelinka, I., Senkerik, R., Bialic-Davendra, M.: Chaos driven evolutionary algorithm for the traveling salesman problem. In: Davendra, D. (ed.) Traveling Salesman Problem, Theory and Applications, pp. 55–70. InTech Publishing, Croatia (2010)CrossRefGoogle Scholar
  5. 5.
    Davendra, D., Senkerik, R., Zelinka, I., Pluhacek, M., Bialic-Davendra, M.: Utilising the chaos-induced discrete self organising migrating algorithm to solve the lot-streaming flowshop scheduling problem with setup time. Soft Computing (2014), doi:10.1007/s00500-014-1219-7Google Scholar
  6. 6.
    Davendra, D., Zelinka, I., Senkerik, R.: Chaos driven evolutionary algorithms for the task of pid control. Computers & Mathematics with Applications 60(4), 1088–1104 (2010)CrossRefMATHMathSciNetGoogle Scholar
  7. 7.
    Garey, M., Johnson, D.: Computers and intractability: A guide to the theory of NP-completeness. Freeman, San Francisco (1979)MATHGoogle Scholar
  8. 8.
    Grabowski, J., Pempera, J.: Sequencing of jobs in some production system. European Journal of Operational Research, 535–550 (2000)Google Scholar
  9. 9.
    Hall, N., Sriskandarayah, C.: A survey of machine scheduling problems with blocking and no-wait in process. Operations Research, 510–525 (1996)Google Scholar
  10. 10.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Computer Engineering Department, Erciyes University, Turkey (2005)Google Scholar
  11. 11.
    Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation 214, 108–132 (2009)CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. Journal of Global Optimization 39, 459–471 (2007)CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    Karaboga, D., Basturk, B.: On the performance of artificial bee colony (abc) algorithm. Applied Soft Computing 8, 687–697 (2008)CrossRefGoogle Scholar
  14. 14.
    Li, J.Q., Pan, Q.K., Tasgetiren, M.F.: A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities. Applied Mathematical Modelling (2013)Google Scholar
  15. 15.
    Lozi, R.: New enhanced chaotic number generators. Indian Journal of Industrial and Applied Mathematics 1(1), 1–23 (2008)MathSciNetGoogle Scholar
  16. 16.
    Lu, Y., Zhou, J., Qin, H., Wang, Y., Zhang, Y.: Chaotic differential evolution methods for dynamic economic dispatch with valve-point effects. Engineering Applications of Artificial Intelligence 24(2), 378–387 (2011)CrossRefGoogle Scholar
  17. 17.
    Matsumoto, M., Nishimura, T.: Mersenne twister: A 623-dimensionally equidistributed uniform pseudorandom number generator. ACM Transaction on Modeling and Computer Simulation 8(1), 3–30 (1998)CrossRefMATHGoogle Scholar
  18. 18.
    Ozer, A.B.: Cide: Chaotically initialized differential evolution. Expert Systems with Applications 37(6), 4632–4641 (2010)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Pan, Q.K., Tasgetiren, M.F., Suganthan, P., Chua, T.: A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Information Sciences 181, 2455–2468 (2011)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Pinedo, M.: Scheduling: theory, algorithms and systems. Prentice Hall, Inc., New Jersey (1995)MATHGoogle Scholar
  21. 21.
    Pluhacek, M., Senkerik, R., Davendra, D., Kominkova Oplatkova, Z., Zelinka, I.: On the behavior and performance of chaos driven pso algorithm with inertia weight. Computers and Mathematics with Applications 66(2), 122–134 (2013)CrossRefMathSciNetGoogle Scholar
  22. 22.
    Pluhacek, M., Senkerik, R., Zelinka, I., Davendra, D.: Chaos pso algorithm driven alternately by two different chaotic maps-an initial study, pp. 2444–2449 (2013)Google Scholar
  23. 23.
    Raaymakers, W., Hoogeveen, J.: Scheduling multipurpose batch process industries with no-wait restrictions by simulated annealing. European Journal of Operational Research, 131–151 (2000)Google Scholar
  24. 24.
    Rajendran, C.: A no-wait flowshop scheduling heuristic to minimize makespan. Journal of the Operational Research Society, 472–478 (1994)Google Scholar
  25. 25.
    Senkerik, R., Pluhacek, M., Davendra, D., Zelinka, I., Kominkova Oplatkova, Z.: Chaos driven evolutionary algorithm: A new approach for evolutionary optimization. International Journal of Mathematics and Computers in Simulation 7(4), 363–368 (2013)Google Scholar
  26. 26.
    Sprott, J.: Chaos and Time-Series Analysis. Oxford University Press, UK (2003)MATHGoogle Scholar
  27. 27.
    Taillard, E.: Benchmarks for basic scheduling problems. European Journal of Operations Research 64, 278–285 (1993)CrossRefMATHGoogle Scholar
  28. 28.
    Tasgetiren, M.F., Pan, Q.K., Suganthan, P., Chen, A.: A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops. Information Sciences 181, 3459–3475 (2011)CrossRefMathSciNetGoogle Scholar
  29. 29.
    Tasgetiren, M.F., Pan, Q.K., Suganthan, P., Oner, A.: A discrete artificial bee colony algorithm for the no-idle permutation flowshop scheduling problem with the total tardiness criterion. Applied Mathematical Modelling 37, 6758–6799 (2013)CrossRefMathSciNetGoogle Scholar
  30. 30.
    Wang, L.: Shop Scheduling with Genetic Algorithms. Tsinghua Univ. Press, Beijing (2003)Google Scholar
  31. 31.
    Yuan, X., Cao, B., Yang, B., Yuan, Y.: Hydrothermal scheduling using chaotic hybrid differential evolution. Energy Conversion and Management 49(12), 3627–3633 (2008)CrossRefGoogle Scholar
  32. 32.
    Zelinka, I., Chadli, M., Davendra, D., Senkerik, R., Pluhacek, M., Lampinen, J.: Do evolutionary algorithms indeed require random numbers? extended study. Advances in Intelligent Systems and Computing 210, 61–75 (2013)CrossRefGoogle Scholar
  33. 33.
    Zuo, X., Fan, Y.: A chaos search immune algorithm with its application to neuro-fuzzy controller design. Chaos, Solitons and Fractals 30(1), 94–109 (2006)CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering and Computer ScienceVŠB-Technical University of OstravaOstrava-PorubaCzech Republic

Personalised recommendations