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The falling tide algorithm: A new multi-objective approach for complex workforce scheduling

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  • Li, Jingpeng
  • Burke, Edmund K.
  • Curtois, Tim
  • Petrovic, Sanja
  • Qu, Rong

Abstract

We present a hybrid approach of goal programming and meta-heuristic search to find compromise solutions for a difficult employee scheduling problem, i.e. nurse rostering with many hard and soft constraints. By employing a goal programming model with different parameter settings in its objective function, we can easily obtain a coarse solution where only the system constraints (i.e. hard constraints) are satisfied and an ideal objective-value vector where each single goal (i.e. each soft constraint) reaches its optimal value. The coarse solution is generally unusable in practise, but it can act as an initial point for the subsequent meta-heuristic search to speed up the convergence. Also, the ideal objective-value vector is, of course, usually unachievable, but it can help a multi-criteria search method (i.e. compromise programming) to evaluate the fitness of obtained solutions more efficiently. By incorporating three distance metrics with changing weight vectors, we propose a new time-predefined meta-heuristic approach, which we call the falling tide algorithm, and apply it under a multi-objective framework to find various compromise solutions. By this approach, not only can we achieve a trade off between the computational time and the solution quality, but also we can achieve a trade off between the conflicting objectives to enable better decision-making.

Suggested Citation

  • Li, Jingpeng & Burke, Edmund K. & Curtois, Tim & Petrovic, Sanja & Qu, Rong, 2012. "The falling tide algorithm: A new multi-objective approach for complex workforce scheduling," Omega, Elsevier, vol. 40(3), pages 283-293.
  • Handle: RePEc:eee:jomega:v:40:y:2012:i:3:p:283-293 DOI: 10.1016/j.omega.2011.05.004
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    References listed on IDEAS

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    1. Burke, Edmund K. & Li, Jingpeng & Qu, Rong, 2010. "A hybrid model of integer programming and variable neighbourhood search for highly-constrained nurse rostering problems," European Journal of Operational Research, Elsevier, vol. 203(2), pages 484-493, June.
    2. Easton, Fred F. & Mansour, Nashat, 1999. "A distributed genetic algorithm for deterministic and stochastic labor scheduling problems," European Journal of Operational Research, Elsevier, vol. 118(3), pages 505-523, November.
    3. Geldermann, Jutta & Bertsch, Valentin & Treitz, Martin & French, Simon & Papamichail, Konstantinia N. & Hämäläinen, Raimo P., 2009. "Multi-criteria decision support and evaluation of strategies for nuclear remediation management," Omega, Elsevier, vol. 37(1), pages 238-251, February.
    4. Various, 1973. "Conference Programs," NBER Chapters,in: The New Realities of the Business Cycle, pages 126-131 National Bureau of Economic Research, Inc.
    5. Barker, Theresa J. & Zabinsky, Zelda B., 2011. "A multicriteria decision making model for reverse logistics using analytical hierarchy process," Omega, Elsevier, vol. 39(5), pages 558-573, October.
    6. Jaumard, Brigitte & Semet, Frederic & Vovor, Tsevi, 1998. "A generalized linear programming model for nurse scheduling," European Journal of Operational Research, Elsevier, vol. 107(1), pages 1-18, May.
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    Cited by:

    1. Li, Jingpeng & Bai, Ruibin & Shen, Yindong & Qu, Rong, 2015. "Search with evolutionary ruin and stochastic rebuild: A theoretic framework and a case study on exam timetabling," European Journal of Operational Research, Elsevier, vol. 242(3), pages 798-806.
    2. Van den Bergh, Jorne & Beliën, Jeroen & De Bruecker, Philippe & Demeulemeester, Erik & De Boeck, Liesje, 2013. "Personnel scheduling: A literature review," European Journal of Operational Research, Elsevier, vol. 226(3), pages 367-385.
    3. Maenhout, Broos & Vanhoucke, Mario, 2013. "An integrated nurse staffing and scheduling analysis for longer-term nursing staff allocation problems," Omega, Elsevier, vol. 41(2), pages 485-499.

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