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Enhancing case-based reasoning for personnel rostering with selected tabu search concepts

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  • G Beddoe

    (University of Nottingham)

  • S Petrovic

    (University of Nottingham)

Abstract

In this paper, we investigate the advantages of using case-based reasoning (CBR) to solve personnel rostering problems. Constraints for personnel rostering problems are commonly categorized as either ‘hard’ or ‘soft’. Hard constraints are those that must be satisfied and a roster that violates none of these constraints is considered to be ‘feasible’. Soft constraints are more flexible and are often used to measure roster quality in terms of staff satisfaction. We introduce a method for repairing hard constraint violations using CBR. CBR is an artificial intelligence paradigm whereby new problems are solved by considering the solutions to previous similar problems. A history of hard constraint violations and their corresponding repairs, which is captured from human rostering experts, is stored and used to solve similar violations in new rosters. The soft constraints are not defined explicitly. Their treatment is captured implicitly during the repair of hard constraint violations. The knowledge in the case-base is combined with selected tabu search concepts in a hybrid meta-heuristic algorithm. Experiments on real-world data from a UK hospital are presented. The results show that CBR can guide a meta-heuristic algorithm towards feasible solutions with high staff satisfaction, without the need to explicitly define soft constraint objectives.

Suggested Citation

  • G Beddoe & S Petrovic, 2007. "Enhancing case-based reasoning for personnel rostering with selected tabu search concepts," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(12), pages 1586-1598, December.
    • Handle: RePEc:pal:jorsoc:v:58:y:2007:i:12:d:10.1057_palgrave.jors.2602304
    DOI: 10.1057/palgrave.jors.2602304
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    Cited by:

    1. Edmund Burke & Jingpeng Li & Rong Qu, 2012. "A Pareto-based search methodology for multi-objective nurse scheduling," Annals of Operations Research, Springer, vol. 196(1), pages 91-109, July.
    2. Burke, Edmund K. & Curtois, Tim, 2014. "New approaches to nurse rostering benchmark instances," European Journal of Operational Research, Elsevier, vol. 237(1), pages 71-81.
    3. Edmund K. Burke & Timothy Curtois & Rong Qu & Greet Vanden Berghe, 2013. "A Time Predefined Variable Depth Search for Nurse Rostering," INFORMS Journal on Computing, INFORMS, vol. 25(3), pages 411-419, August.
    4. Sanja Petrovic & Greet Berghe, 2012. "A comparison of two approaches to nurse rostering problems," Annals of Operations Research, Springer, vol. 194(1), pages 365-384, April.
    5. E K Burke & T Curtois & R Qu & G Vanden Berghe, 2010. "A scatter search methodology for the nurse rostering problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(11), pages 1667-1679, November.
    6. E K Burke & T Curtois & L F van Draat & J-K van Ommeren & G Post, 2011. "Progress control in iterated local search for nurse rostering," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(2), pages 360-367, February.

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