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Case-Oriented Paper

An estimation of distribution algorithm with intelligent local search for rule-based nurse rostering

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Pages 1574-1585 | Received 01 Jan 2006, Accepted 01 Aug 2006, Published online: 21 Dec 2017

References

  • AickelinUAn indirect genetic algorithm for set covering problemsJ Opnl Res Soc2002511118112610.1057/palgrave.jors.2601317
  • AickelinUDowslandKExploiting problem structure in a genetic algorithm approach to a nurse rostering problemJ Scheduling2000313915310.1002/(SICI)1099-1425(200005/06)3:3<139::AID-JOS41>3.0.CO;2-2
  • AickelinUDowslandKAn indirect genetic algorithm for a nurse scheduling problemComput Opns Res20043176177810.1016/S0305-0548(03)00034-0
  • AickelinUWhitePBuilding better nurse scheduling algorithmsAnn Opns Res200412815917710.1023/B:ANOR.0000019103.31340.a6
  • Bard JF and Purnomo W (2007). A cyclic preference scheduling of nurses using a Lagrangian-based heuristic. J Scheduling 10: (in press).
  • BeddoeGPetrovicSSelecting and weighting features using a genetic algorithm in a case-based reasoning approach to personnel rosteringEur J Opnl Res200617564967110.1016/j.ejor.2004.12.028
  • Bosman PAN and Thierens D (2000). Expanding from discrete to continuous estimation of distribution algorithms: the IDEA. In: Deb Schoenauer M et al (eds). Parallel Problem Solving from Nature, Springer Lecture Notes in Computer Science, Vol. 1917. Springer: Berlin, pp 767–776.
  • BruscoMJJacobsLWCost analysis of alternative formulations for personnel scheduling in continuously operating organisationsEur J Opnl Res19958624926110.1016/0377-2217(94)00063-I
  • Burke EK, Causmaecker P and Vanden Berghe G (1999). A hybrid tabu search algorithm for the nurse rostering problem. In: Mckay B (ed). Simulated Evolution and Learning, Springer Lecture Notes in Artificial Intelligence, Vol. 1585. Springer: Berlin, pp 187–194.
  • BurkeEKCausmaeckerPVanden BergheGVan LandeghemHThe state of the art of nurse rosteringJ Scheduling2004744149910.1023/B:JOSH.0000046076.75950.0b
  • BurkeEKCowlingPCausmaeckerPVanden BergheGA memetic approach to the nurse rostering problemAppl Intel20011519921410.1023/A:1011291030731
  • BurkeEKKendallGNewallJHartERossPSchulenburgSHyper-heuristics: an emerging direction in modern search technologyHandbook of Meta-Heuristics2003451470
  • BurkeEKKendallGSoubeigaEA tabu-search hyperheuristic for timetabling and rosteringJ Heuristics2003945147010.1023/B:HEUR.0000012446.94732.b6
  • Burke EK, MacCollum B, Meisols A, Petrovic S and Qu R (2006a). A graph-based hyperheuristic for educational timetabling problems. Eur J Opnl Res (in press).
  • BurkeEKPetrovicSQuRCase based heuristic selection for timetabling problemsJ Scheduling2006911513210.1007/s10951-006-6775-y
  • CheangBLiHLimARodriguesBNurse rostering problems—a bibliographic surveyEur J Opnl Res200315144746010.1016/S0377-2217(03)00021-3
  • DorigoMStützleTAnt Colony Optimization2004
  • DowslandKThompsonJMSolving a nurse scheduling problem with knapsacks, networks and tabu searchJ Opnl Res Soc20005182583310.1057/palgrave.jors.2600970
  • Dowsland K, Soubeiga E and Burke EK (2006). A simulated annealing hyper-heuristic for determining shipper sizes. Eur J Opnl Res (in press).
  • EastonFFMansourNA distributed genetic algorithm for deterministic and stochastic labor scheduling problemsEur J Opnl Res199911850552310.1016/S0377-2217(98)00327-0
  • GutjahrWJRaunerMSAn ACO algorithm for a dynamic regional nurse-scheduling problem in AustriaComputers Opns Res20073464266610.1016/j.cor.2005.03.018
  • IkegamiANiwaAA subproblem-centric model and approach to the nurse rostering problemMath Programming20039751754110.1007/s10107-003-0426-2
  • Kawanaka H et al (2001). Genetic algorithm with the constraints for nurse scheduling problem. In: Proceedings of Congress on Evolutionary Computation. IEEE Service Center: New Jersey, pp 1123–1130.
  • Kendall G and Mohd Hussin N (2005). Tabu search hyper-heuristic approach to the examination timetabling problem at the MARA University of Technology. In: Burke EK and Trick M (eds). Practice and Theory of Automated Timetabling. Springer Lecture Notes in Computer Science, Vol. 3616. Springer: Berlin, pp 270–293.
  • KrasnogorNSmithJA tutorial for competent memetic algorithms: model taxonomy, and design issuesIEEE Trans Evol Comput2005947448810.1109/TEVC.2005.850260
  • Krasnogor N, Hart W and Smith J (eds) (2004). Recent Advances in Memetic Algorithms and Related Search Technologies. Springer: Berlin, 2004.
  • LarranagaPA review on estimation of distribution algorithmsEstimation of Distribution Algorithm: A New Tool for Evolutionary Computation200257100
  • LarranagaPLozanoJAEstimation of Distribution Algorithms2002
  • Li J and Aickelin U (2004). The application of Bayesian optimization and classifier systems in nurse scheduling. In: Yao X et al. (eds). Parallel Problem Solving from Nature. Springer Lecture Notes in Computer Science, Vol. 3242. Springer: Berlin, pp 581–590.
  • LiJKwanRSKA fuzzy genetic algorithm for driver schedulingEur J Opnl Res200314733434410.1016/S0377-2217(02)00564-7
  • LiJKwanRSKA self-adjusting algorithm for driver schedulingJ Heuristics20051135136710.1007/s10732-005-2220-1
  • Meyer auf'm Hofe H (2001). Solving rostering tasks as constraint optimization. In: Burke EK and Erben W (eds). Practice and Theory of Automated Timetabling, Third International Conference, Springer Lecture Notes in Computer Science, Vol. 2079. Springer: Berlin, pp 191–212.
  • MühlenbeinHMahnigTFDA—A scalable evolutionary algorithm for the optimization of additively decomposed functionsEvol Comput1999735337610.1162/evco.1999.7.4.353
  • PearlJProbabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference1988
  • PelikanMHierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithm2005
  • PelikanMGoldbergDELoboFGA survey of optimization by building and using probabilistic modelsComput Optim Appl20022152010.1023/A:1013500812258
  • Rattadilok P, Gaw A and Kwan RSK (2005). Distributed choice function hyper-heuristics for timetabling and scheduling. In: Burke EK and Trick M (eds). Practice and Theory of Automated Timetabling, Springer Lecture Notes in Computer Science, Vol. 3616. Springer: Berlin, pp 51–67.
  • RossPHyper-heuristicsSearch Methodologies: Introductory Tutorials in Optimization and Decision Support Methodologies2005
  • Ross P, Marín-Blázquez JG, Schulenburg S and Hart E (2003). Learning a procedure that can solve hard bin-packing problems: a new GA-based approach to hyper-heuristics. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2003), Springer Lecture Notes in Computer Science, Vol. 2724. Springer: Berlin, pp 1295–1306.
  • SastryKKendallGGoldbergDGenetic algorithmsSearch Methodologies: Introductory Tutorials in Optimisation Decision Support and Search Techniques200597125
  • TienJMKamiyamaAOn manpower scheduling algorithmsSoc Indust Appl Math198224275287
  • WarnerMPrawdaJA mathematical programming model for scheduling nursing personnel in a hospitalMngt Sci19721941142210.1287/mnsc.19.4.411

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