Abstract
Most of the current search techniques represent approaches that are largely adapted for specific search problems. There are many real-world scenarios where the development of such bespoke systems is entirely appropriate. However, there are other situations where it would be beneficial to have methodologies which are generally applicable to more problems. One of our motivating goals for investigating hyper-heuristic methodologies is to provide a more general search framework that can be easily and automatically employed on a broader range of problems than is currently possible. In this paper, we investigate a simulated annealing hyper-heuristic methodology which operates on a search space of heuristics and which employs a stochastic heuristic selection strategy and a short-term memory. The generality and performance of the proposed algorithm is demonstrated over a large number of benchmark datasets drawn from two very different and difficult problems, namely; course timetabling and bin packing. The contribution of this paper is to present a method which can be readily (and automatically) applied to different problems whilst still being able to produce results on benchmark problems which are competitive with bespoke human designed tailor made algorithms for those problems.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdullah S, Burke EK, McCollum B (2007) Using a randomised iterative improvement algorithm with composite neighbourhood structures for the university course timetabling problem. In: Doerner KF, Gendreau M, Greistorfer P, Gutjahr G, Hartl RF, Reimann M (eds) Metaheuristics—progress in complex systems optimization. Springer, New York, pp 153–169
Alvim ACF, Ribeiro CC, Glover F, Aloise DJ (2004) A hybrid improvement heuristic for the one-dimensional bin packing problem. J Heuristics 10: 205–229
Asmuni H, Burke EK, Garibaldi JM (2005) Fuzzy multiple heuristic ordering for course timetabling. In: Proceedings of the 5th United Kingdom workshop on computational intelligence (UKCI05), London, UK, pp 302–309
Bai R, Kendall G (2005) An investigation of automated planograms using a simulated annealing based hyper-heuristic. In: Ibaraki T, Nonobe K, Yagiura M (eds) Metaheuristics: progress as real problem solver—(Operations research/computer science interface series, vol 32). Springer, New York, pp 87–108
Belov G, Scheithauer G (2006) A branch-and-cut-and-price algorithm for one-dimensional stock cutting and two-dimensional two-stage cutting. Eur J Oper Res 171(1): 85–106
Burke EK, Kendall G (2005) Search methodologies: introductory tutorials in optimization and decision support techniques. Kluwer, Dordrecht
Burke EK, Bykov Y, Newall J P, Petrovic S (2003a) A time-predefined approach to course timetabling. Yugosl J Oper Res 13(2): 139–151
Burke EK, Kendall G, Soubeiga E (2003b) A tabu-search hyperheuristic for timetabling and rostering. J Heuristics 9: 451–470
Burke EK, Petrovic S, Qu R (2006) Case based heuristic selection for timetabling problems. J Sched 9(2): 115–132
Burke EK, McCollum B, Meisels A, Petrovic S, Qu R (2007) A graph-based hyper-heuristic for timetabling problems. Eur J Oper Res 176(1): 177–192
Burke EK, Hyde M, Kendall G, Ochoa G, Ozcan E, Woodward JR (2009) A classification of hyper- heuristic approaches. In: Gendreau M, Potvin J-Y (eds) Handbook of metaheursistics. Springer, New York, pp 449–468
Burke EK, Hyde M, Kendall G, Woodward JR (2010) A genetic programming hyper-heuristic approach for evolving 2-D strip packing heuristics. IEEE Trans Evol Comput 14(6): 942–958
Chiarandini M, Birattari M, Socha K, Rossi-Doria O (2006) An effective hybrid algorithm for university course timetabling. J Sched 9(5): 403–432
Chiarandini M, Stutzle T (2003) A landscape analysis for a hybrid approximate algorithm on a timetabling problem. TU Darmstadt Technical Report, AIDA-03-05
Conover WJ (1999) Practical nonparametric statistics, 3rd edn. Wiley, New York
Cowling P, Kendall G, Soubeiga E (2001) A hyperheuristic approach to scheduling a sales summit. In: Burke EK, Erben W (eds) Selected papers of the 3rd international conference on the practice and theory of automated timetabling, Lecture Notes in computer science series, vol 2079. Springer, pp 176–190
Crowston WB, Glover F, Thompson GL, Trawick JD (1963) Probabilistic and parametric learning combinations of local job shop scheduling rules. ONR Research Memorandum, GSIA, Carnegie Mellon University, Pittsburgh, p 117
Dowsland KA, Soubeiga E, Burke EK (2006) A simulated annealing hyper-heuristic for determining shipper sizes. Eur J Oper Res 179(3): 759–774
Falkenauer E (1996) A hybrid grouping genetic algorithm for bin packing. J Heuristics 2: 5–30
Falkenauer E (1998) Genetic algorithms and grouping problems. Wiley, New York
Fisher H, Thompson GL (1963) Probabilistic learning combinations of local job-shop scheduling rules. In: Muth JF, Thompson GL (eds) Industrial scheduling. Prentice-Hall, Englewood Cliffs, NJ, pp 225–251
Fleszar K, Hindi KS (2002) New heuristics for one-dimensional bin-packing. Comput Oper Res 29(7): 821–839
Glover F, Kochenberger G (2003) Handbook of metaheuristics. Kluwer, Dordrecht
Han L, Kendall G (2003) Guided operators for a hyper-heuristic genetic algorithm. In: AI 2003: advances in artificial intelligence: the proceedings of 16th Australian conference on AI. Lecture notes in computer science, vol 2903. Springer, pp 807–820
Hansen P, Mladenovic N, Moreno Perez JA (2008) Variable neighbourhood search: methods and applications. 4OR-A Q J Oper Res 6: 319–360
Hart E, Ross P, Nelson JA (1998) Solving a real-world problem using an evolving heuristically driven schedule builder. Evol Comput 6(1): 61–80
Kitano H (1990) Designing neural networks using genetic algorithms with graph generation system. Complex Syst 4: 461–476
Kostuch P (2004) The university course timetabling problem with a 3-phase approach. In: Burke EK, Trick M (eds) The practice and theory of automated timetabling V, Lecture notes in computer science, vol 3616. Springer, Berlin, pp 109–125
Lourenco HR, Martin OC, Stutzle T (2003) Iterated local search. In: Glover F, Kochenberger G (eds) Handbook of metaheuristics. Kluwer, Dordrecht, pp 321–354
Lundy M, Mees A (1986) Convergence of an annealing algorithm. Math Program 34: 111–124
Martello S, Toth P (1990) Knapsack problems: algorithms and computer implementations. Wiley, New York
Martello S, Toth P (1990) Lower pounds and reduction procedures for the bin packing problem. Discret Appl Math 28: 59–70
McMullan P (2007) An extended implementation of the Great Deluge Algorithm for course timetabling. Lecture notes in computer science, vol 4487. Springer, Berlin, pp 538–545
Metaheuristic Network (2003) International timetabling competition: Competition results http://www.idsia.ch/Files/ttcomp2002/results.htm. Accessed 11 October 2010
Mockus J (1989) Bayesian approach to global optimization. Kluwer, Dordrecht
Mockus J (1994) Application of bayesian approach to numerical methods of global and stochastic optimization. J Glob Optim 4(4): 347–366
Mockus J (1997) Bayesian heuristic approach to discrete and global optimization. Kluwer, Dordrecht
Mockus J (2000) A set of examples of global and discrete optimization: application of bayesian heuristic approach. Kluwer, Dordrecht
Nareyek A (2003) Choosing search heuristics by non-stationary reinforcement learning. In: Resende MGC, de Sousa JP (eds) Metaheuristics: computer decision-making. Kluwer, Dordrecht, pp 523–544
Qu R, Burke EK (2009) Hybridisations within a graph based hyper-heuristic framework for university timetabling problems. J Oper Res Soc 60: 1273–1285
Rattadilok P, Gaw A, Kwan RSK (2005) Distributed choice function hyper-heuristics for timetabling and scheduling. In: Burke EK, Trick M (eds) Selected papers from the 5th international conference on the practice and theory of automated timetabling. Lecture notes in computer science series, vol 3616. Springer, Berlin, pp 51–70
Ropke S, Pisinger D (2006) An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transp Sci 40(4): 455–472
Ross P (2005) Hyper-heuristics. In: Burke EK, Kendall G (eds) Search methodologies: introductory tutorials in optimization and decision support techniques. Springer, Berlin, pp 529–556
Ross P, Marin-Blazquez JG, Schulenburg S, Hart E (2003) Learning a procedure that can solve hard bin-packing problems: a new GA-based approach to hyper-heuristics. In: Proceeding of the genetic and evolutionary computation conference, GECCO 2003. Springer, Berlin, pp 1295–1306
Rossi-Doria O, Blum C, Knowles J, Samples M, Socha K, Paechter B (2002) A local search for automated timetabling. In: Proceedings of the 4th international conference on the practice and theory of automated timetabling [PATAT 2002] (pp 124–127)
Runarsson TP, Yao X (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Trans Evol Comput 4(3): 344–354
Scholl A, Klein R, Jurgens C (1997) BISON: a fast hybrid procedure for exactly solving the one-dimensional bin packing problem. Comput Oper Res 24(7): 627–645
Schwerin P, Wascher G (1997) The bin-packing problem: a problem generator and some numerical experiments with FFD packing and MTP. Int Trans Oper Res 4(5/6): 377–389
Socha K, Knowles J, Samples M (2002) A max-min ant system for the university course timetabling problem. In: Proceedings of the 3rd international workshop on ant algorithm, ANTS 2002. Lecture notes in computer science, vol 2463, pp 1–13
Soubeiga E (2003) Development and application of hyperheuristics to personnel scheduling. PhD Thesis, The University of Nottingham, UK
Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, Cambridge, MA
Terashima-Marin H, Ross P, Valenzuela-Rendon M (1999) Evolution of constraint satisfaction strategies in examination timetabling. In: Proceedings of the genetic and evolutionary computation conference, GECCO 1999. Morgan Kaufmann, Los Altos, CA, pp 635–642
Venkatraman S, Yen GG (2005) A generic framework for constrained optimization using genetic algorithms. IEEE Trans Evol Comput 9(4): 424–435
Waescher G, Gau T (1996) Heuristics for the integer one-dimensional cutting stock problem: a computational study. OR Spektrum 18: 131–144
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Bai, R., Blazewicz, J., Burke, E.K. et al. A simulated annealing hyper-heuristic methodology for flexible decision support. 4OR-Q J Oper Res 10, 43–66 (2012). https://doi.org/10.1007/s10288-011-0182-8
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10288-011-0182-8