Skip to main content
SpringerLink
Log in
Book cover

Recent Advances in Swarm Intelligence and Evolutionary Computation pp 121–137Cite as

Discrete Cuckoo Search Applied to Job Shop Scheduling Problem

  • Chapter
  • First Online:

Part of the book series: Studies in Computational Intelligence ((SCI,volume 585))

Abstract

Discrete Cuckoo Search (DCS) algorithm is applied to solve combinatorial optimization problems. In this chapter we discuss how DCS solves the Job Shop Scheduling Problem (JSSP), one of the most difficult NP-hard combinatorial optimization problems. DCS is recently developed by Ouaarab et al. in 2013, based on Cuckoo Search (CS) which was proposed by Yang and Deb in 2009. DCS seeks solutions, in the discrete search space, via Lévy flights and a switching parameter p a of the worst solution in the population. Its search uses a subtle balance between local and global random walks. This first version of DCS for JSSP is designed without using an advanced local search method or hybrid with other metaheuristics. Our experimental results show that DCS can find the optimum solutions for a number of JSSP instances.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Applegate, D., Cook, W.: A computational study of the job-shop scheduling problem. ORSA J. comput. 3(2), 149–156 (1991)

    Article  MATH  Google Scholar 

  2. Manne, A.S.: On the job-shop scheduling problem. Oper. Res. 8(2), 219–223 (1960)

    Article  MathSciNet  Google Scholar 

  3. Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Comput. Surv. (CSUR) 35(3), 268–308 (2003)

    Article  Google Scholar 

  4. Gandomi, A.H., Yang, X.S., Talatahari, S., Alavi, A.H.: Metaheuristic applications in structures and infrastructures. Newnes (2013)

    Google Scholar 

  5. Yang, X.S.: Nature-inspired metaheuristic algorithms. Luniver Press, Bristol (2010)

    Google Scholar 

  6. Ouaarab, A., Ahiod, B., Yang, X.S.: Improved and discrete cuckoo search for solving the travelling salesman problem. In: Cuckoo Search and Firefly Algorithm, pp. 63–84. Springer, Berlin (2014)

    Google Scholar 

  7. Davis, L., et al.: Handbook of genetic algorithms, vol. 115. Van Nostrand Reinhold, New York (1991)

    Google Scholar 

  8. Sivanandam, S., Deepa, S.: Genetic Algorithm Optimization Problems. Springer, Berlin (2008)

    Google Scholar 

  9. Glover, F., Laguna, M.: Tabu search. Springer, Berlin (1999)

    Google Scholar 

  10. Van Laarhoven, P.J., Aarts, E.H.: Simulated annealing. Springer, Berlin (1987)

    Google Scholar 

  11. Dorigo, M., Blum, C.: Ant colony optimization theory: A survey. Theoret. Comput. Sci. 344(2), 243–278 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  12. Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer, Berlin (2010)

    Google Scholar 

  13. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (abc) algorithm. Appl. Soft Comput. 8(1), 687–697 (2008)

    Article  Google Scholar 

  14. Mucherino, A., Seref, O.: Monkey search: a novel metaheuristic search for global optimization. In: Data Mining, Systems Analysis and Optimization in Biomedicine, vol. 953, pp. 162–173. AIP Publishing, New York (2007)

    Google Scholar 

  15. Geem, Z.W., Kim, J.H., Loganathan, G.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)

    Article  Google Scholar 

  16. Yang, X.S.: Firefly algorithms for multimodal optimization. In: Stochastic algorithms: foundations and applications, pp. 169–178. Springer, Berlin (2009)

    Google Scholar 

  17. Shah-Hosseini, H.: The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. Int. J. Bio-Inspired Comput. 1(1), 71–79 (2009)

    Article  Google Scholar 

  18. Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer, Berlin (2010)

    Google Scholar 

  19. Yang, X.S., Deb, S.: Cuckoo search via lévy flights. In: Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, pp. 210–214. IEEE, New York (2009)

    Google Scholar 

  20. Yang, X.S.: Nature-Inspired Optimizaton Algorithms. Elsevier (2014)

    Google Scholar 

  21. Yang, X.S., Karamanoglu, M., He, X.: Flower pollination algorithm: a novel approach for multiobjective optimization. Eng. Optim. 46, 1222–1237 (2014)

    Article  MathSciNet  Google Scholar 

  22. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. Evolut. Comput. IEEE Trans. 1(1), 67–82 (1997)

    Article  Google Scholar 

  23. Dell’Amico, M., Trubian, M.: Applying tabu search to the job-shop scheduling problem. Ann. Oper. Res. 41(3), 231–252 (1993)

    Article  MATH  Google Scholar 

  24. Taillard, E.D.: Parallel taboo search techniques for the job shop scheduling problem. ORSA J. Comput. 6(2), 108–117 (1994)

    Article  MATH  Google Scholar 

  25. Nowicki, E., Smutnicki, C.: A fast taboo search algorithm for the job shop problem. Manage. Sci. 42(6), 797–813 (1996)

    Article  MATH  Google Scholar 

  26. Nowicki, E., Smutnicki, C.: An advanced tabu search algorithm for the job shop problem. J. Sched. 8(2), 145–159 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  27. Van Laarhoven, P.J., Aarts, E.H., Lenstra, J.K.: Job shop scheduling by simulated annealing. Oper. Res. 40(1), 113–125 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  28. Davis, L.: Job shop scheduling with genetic algorithms. In: Proceedings of an International Conference on Genetic Algorithms and Their Applications, vol. 140 (1985)

    Google Scholar 

  29. Della Croce, F., Tadei, R., Volta, G.: A genetic algorithm for the job shop problem. Comput. Oper. Res. 22(1), 15–24 (1995)

    Google Scholar 

  30. Gonçalves, J.F., de Magalhães Mendes, J.J., Resende, M.G.: A hybrid genetic algorithm for the job shop scheduling problem. Eur. J. Oper. Res. 167(1), 77–95 (2005)

    Google Scholar 

  31. Lin, T.L., Horng, S.J., Kao, T.W., Chen, Y.H., Run, R.S., Chen, R.J., Lai, J.L., Kuo, I., et al.: An efficient job-shop scheduling algorithm based on particle swarm optimization. Expert Syst. Appl. 37(3), 2629–2636 (2010)

    Article  Google Scholar 

  32. Huang, K.L., Liao, C.J.: Ant colony optimization combined with taboo search for the job shop scheduling problem. Comput. Oper. Res. 35(4), 1030–1046 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  33. Zhang, C.Y., Li, P., Rao, Y., Guan, Z.: A very fast ts/sa algorithm for the job shop scheduling problem. Comput. Oper. Res. 35(1), 282–294 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  34. Aiex, R.M., Binato, S., Resende, M.G.: Parallel grasp with path-relinking for job shop scheduling. Parallel Comput. 29(4), 393–430 (2003)

    Article  MathSciNet  Google Scholar 

  35. Binato, S., Hery, W., Loewenstern, D., Resende, M.: A grasp for job shop scheduling. In: Essays and Surveys in Metaheuristics, pp. 59–79. Springer, Berlin (2002)

    Google Scholar 

  36. Chong, C.S., Low, M.Y.H., Sivakumar, A.I., Gay, K.L.: A bee colony optimization algorithm to job shop scheduling. In: Simulation Conference, 2006. WSC 06. Proceedings of the Winter, pp. 1954–1961. IEEE, New York (2006)

    Google Scholar 

  37. Sha, D., Hsu, C.Y.: A hybrid particle swarm optimization for job shop scheduling problem. Comput. Ind. Eng. 51(4), 791–808 (2006)

    Article  Google Scholar 

  38. Beasley, J.E.: Or-library: distributing test problems by electronic mail. J. Oper. Res. Soc. pp. 1069–1072 (1990)

    Google Scholar 

  39. Pinedo, M.L.: Scheduling: theory, algorithms, and systems. Springer, Berlin (2012)

    Google Scholar 

  40. T’kindt, V., Scott, H., Billaut, J.C.: Multicriteria scheduling: theory, models and algorithms. Springer, Berlin (2006)

    Google Scholar 

  41. Ponsich, A.: Coello Coello, C.A.: A hybrid differential evolution—tabu search algorithm for the solution of job-shop scheduling problems. Appl. Soft Comput. 13(1), 462–474 (2013)

    Article  Google Scholar 

  42. Błażewicz, J., Domschke, W., Pesch, E.: The job shop scheduling problem: Conventional and new solution techniques. Eur. J. Oper. Res. 93(1), 1–33 (1996)

    Article  MATH  Google Scholar 

  43. Jain, A.S., Meeran, S.: Deterministic job-shop scheduling: Past, present and future. Eur. J. Oper. Res. 113(2), 390–434 (1999)

    Article  MATH  Google Scholar 

  44. Cheng, T., Peng, B., Lü, Z.: A hybrid evolutionary algorithm to sovle the job shop scheduling problem. Ann. Oper. Res. pp. 1–15, 2013. http://link.springer.com/article/10.1007

  45. Roy, B., Sussmann, B.: Les problemes d’ordonnancement avec contraintes disjonctives. Note ds 9 (1964)

    Google Scholar 

  46. Esquirol, P., Lopez, P., professeur de robotique) Lopez, P.: L’ordonnancement. Économica (1999)

    Google Scholar 

  47. Herrmann, J.W.: Handbook of production scheduling, vol. 89. Springer, Berlin (2006)

    Google Scholar 

  48. Payne, R.B.: The cuckoos, vol. 15. Oxford University Press, Oxford (2005)

    Google Scholar 

  49. Shlesinger, M.F., Zaslavsky, G.M., Frisch, U.: Lévy flights and related topics in physics. In: Levy flights and related topics in Physics, vol. 450 (1995)

    Google Scholar 

  50. Ouaarab, A., Ahiod, B., Yang, X.S.: Discrete cuckoo search algorithm for the travelling salesman problem. Neural Comput. Appl. pp. 1–11 (2013)

    Google Scholar 

  51. Ouaarab, A., Ahiod, B., Yang, X.S.: Random-key cuckoo search for the travelling salesman problem. Soft Comput. pp. 1–8 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LRIT, Associated Unit to the CNRST (URAC 29), Mohammed V-Agdal University, B.P. 1014, Rabat, Morocco

    Aziz Ouaarab & Belaïd Ahiod

  2. School of Science and Technology, Middlesex University, The Burroughs, London, NW4 4BT, UK

    Xin-She Yang

Authors
  1. Aziz Ouaarab
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Belaïd Ahiod
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xin-She Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aziz Ouaarab .

Editor information

Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, United Kingdom

    Xin-She Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Ouaarab, A., Ahiod, B., Yang, XS. (2015). Discrete Cuckoo Search Applied to Job Shop Scheduling Problem. In: Yang, XS. (eds) Recent Advances in Swarm Intelligence and Evolutionary Computation. Studies in Computational Intelligence, vol 585. Springer, Cham. https://doi.org/10.1007/978-3-319-13826-8_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-13826-8_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13825-1

  • Online ISBN: 978-3-319-13826-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation