Memetic Computing

, Volume 6, Issue 2, pp 77–84 | Cite as

Tailoring hyper-heuristics to specific instances of a scheduling problem using affinity and competence functions

  • Abdellah SalhiEmail author
  • José Antonio Vázquez Rodríguez
Regular research paper


Hyper-heuristics are high level heuristics which coordinate lower level ones to solve a given problem. Low level heuristics, however, are not all as competent/good as each other at solving the given problem and some do not work together as well as others. Hence the idea of measuring how good they are (competence) at solving the problem and how well they work together (their affinity). Models of the affinity and competence properties are suggested and evaluated using previous information on the performance of the simple low level heuristics. The resulting model values are used to improve the performance of the hyper-heuristic by tailoring it not only to the specific problem but the specific instance being solved. The test case is a hard combinatorial problem, namely the Hybrid Flow Shop scheduling problem. Numerical results on randomly generated as well as real-world instances are included.


Hybrid Flow Shop Scheduling Genetic Algorithm Hyper-heuristics 


  1. 1.
    Cowling P, Kendall G, Soubeiga E (2000) A hyperheuristic approach to scheduling a sales summit. In: Burke EK, Erben W (ed) Proceedings of the third international conference on the practice and theory of automated timetabling III. Lecture notes in computer science, vol 2079. Springer, Berlin, pp 176–190Google Scholar
  2. 2.
    Soubeiga E (2003) Development and application of hyperheuristics to personnel scheduling. PhD thesis, School of Computer Science and Information Technology, The University of NottinghamGoogle Scholar
  3. 3.
    Burke EK, Hyde M, Kendall G, Ochoa G, Ozcan E, Hyper-heursitics RQU (2010) A survey of the state of the art. Technical report, School of Computer Science and Information Technology, University of Nottingham, UKGoogle Scholar
  4. 4.
    Burke EK, Hyde M, Kendall G, Ochoa G, Ozcani E, Woodward JR (2009) Exploring hyper-heursitic methodologies with genetic programming. In: Mumford C, Jain L (eds) Computational intelligence: collaboration, fusion and emergence. Springer, BerlinGoogle Scholar
  5. 5.
    Burke E, Soubeiga E (2003) Scheduling nurses using a tabu-search hyperheuristic. In: Proceedings of the 1st multidisciplinary international conference on scheduling: theory and applications (MISTA 2003)Google Scholar
  6. 6.
    Cowling P, Kendall G, Han L (2002) An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem. In: Proceedings of congress on evolutionary computation (CEC2002). IEEE, New York, pp 1185–1190Google Scholar
  7. 7.
    Burke E, Kendall G, Landa SD, O‘Brien R, Soubeiga E (2005) An ant algorithm hyperheuristic for the project presentation scheduling problem. In: Proceedings of the congress on evolutionary computation (CEC 2005). IEEE Press, New York, pp 2263–2270Google Scholar
  8. 8.
    Ayob M, Kendall G (2003) A monte carlo hyper-heuristic to optimise component placement sequencing for multi head placement machine. In: Proceedings of the international conference on intelligent technologies, InTech’03, pp 132–141Google Scholar
  9. 9.
    Petrovic S, Qu R (2002) Case-based reasoning as a heuristic selector in a hyper-heuristic for course timetabling problems. In: Proceedings of the 6th international conference on knowledge-based intelligent information engineering systems and applied technologies (KES’02), pp 336–340Google Scholar
  10. 10.
    Burke EK, McCarthy BL, Petrovic S, Qu R (2002) Knowledge discovery in a hyper-heuristic for course timetabling using case-based reasoning. In: Proceedings of the 4th international conference on the practice and theory of automated timetabling (PATAT 2002). Lecture notes in computer science, vol 2740. Springer, Berlin, pp 276–286Google Scholar
  11. 11.
    Petrovic S, Fayad C, Petrovic D (2005) Job shop scheduling with lot-sizing and batching in an uncertain real-world environment. In: Kendall G, Lei L, Pinedo M (eds) Proceedings of the 2nd multidisciplinary international conference on scheduling: theory and applications (MISTA 2005), New York, pp 363–379Google Scholar
  12. 12.
    Hart E, Ross P (1998) A heuristic combination method for solving job-shop scheduling problems. In: Lecture notes in computer sciences (1498). Springer, Berlin, pp 845–854Google Scholar
  13. 13.
    Fang H-L, Ross P, Corne D (1994) A promising hybrid GA/heuristic approach for open-shop scheduling problems. In: Cohn A (ed) 11th european conference on artificial intelligence (ECAI 94). Wiley, New York, pp 590–594Google Scholar
  14. 14.
    Burke E, Dror M, Petrovic S, Qu R (2005) Hybrid graph heuristic within a hyper-heuristic approach to exam timetabling problems. In: Golden BL, Raghavan S, Wasil EA (eds) Proceedings of the 9th informs computing society conference. Springer, Berlin, pp 79–91Google Scholar
  15. 15.
    Vázquez Rodríguez JA (2007) Meta-hyper-heuristics for hybrid flow shops. PhD thesis, Department of Mathematical Sciences, University of Essex, ColchesterGoogle Scholar
  16. 16.
    Michalewicz Z (1992) Genetic algorithms + data structures = evolution programs. Springer, BerlinCrossRefzbMATHGoogle Scholar
  17. 17.
    Goldberg David E (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, MassachusettszbMATHGoogle Scholar
  18. 18.
    Gupta JND (1988) Two-stage hybrid flow shop scheduling problem. Oper Res Soc 39:359–364CrossRefzbMATHGoogle Scholar
  19. 19.
    Hoogeveen JA, Lenstra JK, Veltman B (1996) Preemptive scheduling in a two-stage multiprocessor flow shop is NP-hard. Eur J Oper Res 89:172–175CrossRefzbMATHGoogle Scholar
  20. 20.
    Jin ZH, Ohno K, Ito T, Elmaghraby SE (2002) Scheduling hybrid flowshops in printed circuit board assembly lines. Prod Oper Manag 11:216–230CrossRefGoogle Scholar
  21. 21.
    Sherali HD, Sarin SC, Kodialam MS (1990) Models and algorithms for a two-stage production process. Prod Plan Control 1:27–39CrossRefGoogle Scholar
  22. 22.
    Guinet AG (1991) Textile production systems: a succession of non-identical parallel processor shops. J Oper Res Soc 42:655–671Google Scholar
  23. 23.
    Grabowski J, Pempera J (2000) Sequencing of jobs in some production system. Eur J Oper Res 125:535–550Google Scholar
  24. 24.
    Aghezzaf EH, Van Landeghem H (2002) An integrated model for inventory and production planning in a two-stage hybrid production system. Int J Prod Res 40:4323–4339 Google Scholar
  25. 25.
    Allahverdi A, Al-Anzi FS (2006) Scheduling multi-stage parallel-processor services to minimize average response time. J Oper Res Soc 57:101–110Google Scholar
  26. 26.
    Lu Chen, Li-Feng Xi, Jian-Guo Ca I, Nathalie Bostel, Pierre Dejax (2006) An integrated approach for modeling and solving the scheduling problem of container handling systems. J Zhejiang Univ SCIENCE A 7:234–239CrossRefzbMATHGoogle Scholar
  27. 27.
    Lin HT, Liao CJ (2003) A case study in a two-stage hybrid flow shop with setup time and dedicated machines. Int J Prod Econ 86:133–143CrossRefGoogle Scholar
  28. 28.
    Choong F, Phin-Amnuaisuk S, Alias MY (2011) Metaheuristic methods in hybrid flow shop scheduling problem. Exp Syst Appl 38(9):10787–10793CrossRefGoogle Scholar
  29. 29.
    Pinedo Michael (2002) Scheduling theory, algorithms and systems. Prentice Hall, Englewood CliffszbMATHGoogle Scholar
  30. 30.
    Rodríguez José Antonio Vázquez, Salhi Abdellah (2007) A robust meta-hyper-heuristic approach to hybrid flow shop scheduling. In: Dahal K, Cowling P (eds) Evol Sched. Springer, Berlin, pp 125–142CrossRefGoogle Scholar
  31. 31.
    Hollander M, Wolfe DA (1973) Nonparametric statistical methods. Wiley, New YorkzbMATHGoogle Scholar
  32. 32.
    Riane Fouad, Artiba Abdelhakim, Elmaghraby Salah E (2002) Sequencing a hybrid two-stage flowshop with dedicated machines. Int J Prod Res 40:4353–4380CrossRefzbMATHGoogle Scholar
  33. 33.
    García Rubén Ruíz, Maroto Concepción (2006) A genetic algorithm for hybrid flow shops with sequence dependent setup times and machine eligibility. Eur J Oper Res 169:781–800CrossRefGoogle Scholar
  34. 34.
    Moscato P, Cotta C (2003) A gentle introduction to memetic algorithms, pp 105–144. In: Handbook of Metaheuristics. Kluwer Academic Publishers, DordrechtGoogle Scholar
  35. 35.
    Ong YS, Kean AJ (2004) Meta-Lamarckian learning in memetic algorithms. IEEE Trans Evol Comput 8(2):99–110CrossRefGoogle Scholar
  36. 36.
    Salhi A, Töreyen Ö (2010) A game theory-based multi-agent system for expensive optimisation. In: Tenne Y, Goh C-K (eds) Computational intelligence in optimization—applications and implementations, Chap. 9. Springer, Berlin, pp 212–232Google Scholar
  37. 37.
    Töreyen Ö (2008) A game theory-based multi-agent system for solving complex optimisation problems. Master’s thesis, Department of Mathematical Sciences, University of Essex, ColchesterGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Abdellah Salhi
    • 1
    Email author
  • José Antonio Vázquez Rodríguez
    • 2
  1. 1.Department of Mathematical SciencesThe University of EssexColchesterUK
  2. 2.Department of Computer Science and Information TechnologyThe University of NottinghamNottinghamUK

Personalised recommendations