A simulated annealing hyperheuristic methodology for flexible decision support
 Ruibin Bai,
 Jacek Blazewicz,
 Edmund K. Burke,
 Graham Kendall,
 Barry McCollum
 … show all 5 hide
Rent the article at a discount
Rent now* Final gross prices may vary according to local VAT.
Get AccessAbstract
Most of the current search techniques represent approaches that are largely adapted for specific search problems. There are many realworld 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 hyperheuristic 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 hyperheuristic methodology which operates on a search space of heuristics and which employs a stochastic heuristic selection strategy and a shortterm 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.
 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 onedimensional bin packing problem. J Heuristics 10: 205–229 CrossRef
 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 hyperheuristic. 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 branchandcutandprice algorithm for onedimensional stock cutting and twodimensional twostage cutting. Eur J Oper Res 171(1): 85–106 CrossRef
 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 timepredefined approach to course timetabling. Yugosl J Oper Res 13(2): 139–151 CrossRef
 Burke EK, Kendall G, Soubeiga E (2003b) A tabusearch hyperheuristic for timetabling and rostering. J Heuristics 9: 451–470 CrossRef
 Burke EK, Petrovic S, Qu R (2006) Case based heuristic selection for timetabling problems. J Sched 9(2): 115–132 CrossRef
 Burke EK, McCollum B, Meisels A, Petrovic S, Qu R (2007) A graphbased hyperheuristic for timetabling problems. Eur J Oper Res 176(1): 177–192 CrossRef
 Burke EK, Hyde M, Kendall G, Ochoa G, Ozcan E, Woodward JR (2009) A classification of hyper heuristic approaches. In: Gendreau M, Potvin JY (eds) Handbook of metaheursistics. Springer, New York, pp 449–468
 Burke EK, Hyde M, Kendall G, Woodward JR (2010) A genetic programming hyperheuristic approach for evolving 2D strip packing heuristics. IEEE Trans Evol Comput 14(6): 942–958 CrossRef
 Chiarandini M, Birattari M, Socha K, RossiDoria O (2006) An effective hybrid algorithm for university course timetabling. J Sched 9(5): 403–432 CrossRef
 Chiarandini M, Stutzle T (2003) A landscape analysis for a hybrid approximate algorithm on a timetabling problem. TU Darmstadt Technical Report, AIDA0305
 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 hyperheuristic for determining shipper sizes. Eur J Oper Res 179(3): 759–774 CrossRef
 Falkenauer E (1996) A hybrid grouping genetic algorithm for bin packing. J Heuristics 2: 5–30 CrossRef
 Falkenauer E (1998) Genetic algorithms and grouping problems. Wiley, New York
 Fisher H, Thompson GL (1963) Probabilistic learning combinations of local jobshop scheduling rules. In: Muth JF, Thompson GL (eds) Industrial scheduling. PrenticeHall, Englewood Cliffs, NJ, pp 225–251
 Fleszar K, Hindi KS (2002) New heuristics for onedimensional binpacking. Comput Oper Res 29(7): 821–839 CrossRef
 Glover F, Kochenberger G (2003) Handbook of metaheuristics. Kluwer, Dordrecht
 Han L, Kendall G (2003) Guided operators for a hyperheuristic 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. 4ORA Q J Oper Res 6: 319–360 CrossRef
 Hart E, Ross P, Nelson JA (1998) Solving a realworld problem using an evolving heuristically driven schedule builder. Evol Comput 6(1): 61–80 CrossRef
 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 3phase 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 CrossRef
 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 CrossRef
 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 CrossRef
 Mockus J (1994) Application of bayesian approach to numerical methods of global and stochastic optimization. J Glob Optim 4(4): 347–366 CrossRef
 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 nonstationary reinforcement learning. In: Resende MGC, de Sousa JP (eds) Metaheuristics: computer decisionmaking. Kluwer, Dordrecht, pp 523–544
 Qu R, Burke EK (2009) Hybridisations within a graph based hyperheuristic framework for university timetabling problems. J Oper Res Soc 60: 1273–1285 CrossRef
 Rattadilok P, Gaw A, Kwan RSK (2005) Distributed choice function hyperheuristics 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 CrossRef
 Ross P (2005) Hyperheuristics. In: Burke EK, Kendall G (eds) Search methodologies: introductory tutorials in optimization and decision support techniques. Springer, Berlin, pp 529–556
 Ross P, MarinBlazquez JG, Schulenburg S, Hart E (2003) Learning a procedure that can solve hard binpacking problems: a new GAbased approach to hyperheuristics. In: Proceeding of the genetic and evolutionary computation conference, GECCO 2003. Springer, Berlin, pp 1295–1306
 RossiDoria 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 CrossRef
 Scholl A, Klein R, Jurgens C (1997) BISON: a fast hybrid procedure for exactly solving the onedimensional bin packing problem. Comput Oper Res 24(7): 627–645 CrossRef
 Schwerin P, Wascher G (1997) The binpacking problem: a problem generator and some numerical experiments with FFD packing and MTP. Int Trans Oper Res 4(5/6): 377–389 CrossRef
 Socha K, Knowles J, Samples M (2002) A maxmin 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
 TerashimaMarin H, Ross P, ValenzuelaRendon 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 CrossRef
 Waescher G, Gau T (1996) Heuristics for the integer onedimensional cutting stock problem: a computational study. OR Spektrum 18: 131–144 CrossRef
 Title
 A simulated annealing hyperheuristic methodology for flexible decision support
 Journal

4OR
Volume 10, Issue 1 , pp 4366
 Cover Date
 20120301
 DOI
 10.1007/s1028801101828
 Print ISSN
 16194500
 Online ISSN
 16142411
 Publisher
 SpringerVerlag
 Additional Links
 Topics
 Keywords

 Hyperheuristics
 Simulated annealing
 Bin packing
 Course timetabling
 9008: Computational methods
 Authors

 Ruibin Bai ^{(1)}
 Jacek Blazewicz ^{(2)}
 Edmund K. Burke ^{(3)}
 Graham Kendall ^{(3)}
 Barry McCollum ^{(4)}
 Author Affiliations

 1. Division of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK
 2. Institute of Computing Science, Poznan University of Technology, ul. Piotrowo 2, 60965, Poznan, Poland
 3. School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK
 4. Department of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, BT7 1NN, UK