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An efficient computational method for large scale surgery scheduling problems with chance constraints

Author

Listed:
  • Mahdi Noorizadegan

    (Amirkabir University of Technology)

  • Abbas Seifi

    (Amirkabir University of Technology)

Abstract

We propose an efficient solution method based on a decomposition of set-partitioning formulation of an integrated surgery planning and scheduling problem with chance constraints. The studied problem is characterized by a set of identical operating rooms (ORs), a set of surgeries with uncertain durations, a set of surgeons, and surgery dependent turnover times. The decision variables include the number of ORs to open, assignments of surgeries and surgeons to ORs in admissible periods, and the sequence of surgeries to be performed in a period. The objective is to minimize the cost of opening ORs and the penalties associated with turnover times.In the proposed formulation, the column generation subproblem is decomposed over ORs and time periods. The structure of the subproblem is further exploited and transformed to a shortest path problem. A search algorithm has been devised to efficiently solve the resulting subproblem, subject to some optimality and feasibility conditions. The proposed computational method outperforms the standard chance constrained model and reduces the solution time significantly. Furthermore, extensive simulation experiments have been carried out to compare the performance of three variants of the underlying formulations and evaluate the sensitivity of the decisions to the probability of exceeding a session length.

Suggested Citation

  • Mahdi Noorizadegan & Abbas Seifi, 2018. "An efficient computational method for large scale surgery scheduling problems with chance constraints," Computational Optimization and Applications, Springer, vol. 69(2), pages 535-561, March.
  • Handle: RePEc:spr:coopap:v:69:y:2018:i:2:d:10.1007_s10589-017-9947-0
    DOI: 10.1007/s10589-017-9947-0
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    References listed on IDEAS

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    1. B Zhang & P Murali & M M Dessouky & D Belson, 2009. "A mixed integer programming approach for allocating operating room capacity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(5), pages 663-673, May.
    2. Oleg V. Shylo & Oleg A. Prokopyev & Andrew J. Schaefer, 2013. "Stochastic Operating Room Scheduling for High-Volume Specialties Under Block Booking," INFORMS Journal on Computing, INFORMS, vol. 25(4), pages 682-692, November.
    3. Wang, Yu & Tang, Jiafu & Fung, Richard Y.K., 2014. "A column-generation-based heuristic algorithm for solving operating theater planning problem under stochastic demand and surgery cancellation risk," International Journal of Production Economics, Elsevier, vol. 158(C), pages 28-36.
    4. Sakine Batun & Brian T. Denton & Todd R. Huschka & Andrew J. Schaefer, 2011. "Operating Room Pooling and Parallel Surgery Processing Under Uncertainty," INFORMS Journal on Computing, INFORMS, vol. 23(2), pages 220-237, May.
    5. Cynthia Barnhart & Ellis L. Johnson & George L. Nemhauser & Martin W. P. Savelsbergh & Pamela H. Vance, 1998. "Branch-and-Price: Column Generation for Solving Huge Integer Programs," Operations Research, INFORMS, vol. 46(3), pages 316-329, June.
    6. Min, Daiki & Yih, Yuehwern, 2010. "Scheduling elective surgery under uncertainty and downstream capacity constraints," European Journal of Operational Research, Elsevier, vol. 206(3), pages 642-652, November.
    7. Zheng Zhang & Xiaolan Xie, 2015. "Simulation-based optimization for surgery appointment scheduling of multiple operating rooms," IISE Transactions, Taylor & Francis Journals, vol. 47(9), pages 998-1012, September.
    8. Brian T. Denton & Andrew J. Miller & Hari J. Balasubramanian & Todd R. Huschka, 2010. "Optimal Allocation of Surgery Blocks to Operating Rooms Under Uncertainty," Operations Research, INFORMS, vol. 58(4-part-1), pages 802-816, August.
    9. Shabbir Ahmed & Dimitri J. Papageorgiou, 2013. "Probabilistic Set Covering with Correlations," Operations Research, INFORMS, vol. 61(2), pages 438-452, April.
    10. Serhat Gul & Brian T. Denton & John W. Fowler, 2015. "A Progressive Hedging Approach for Surgery Planning Under Uncertainty," INFORMS Journal on Computing, INFORMS, vol. 27(4), pages 755-772, November.
    11. Fanwen Meng & Jin Qi & Meilin Zhang & James Ang & Singfat Chu & Melvyn Sim, 2015. "A Robust Optimization Model for Managing Elective Admission in a Public Hospital," Operations Research, INFORMS, vol. 63(6), pages 1452-1467, December.
    12. Yongjia Song & James R. Luedtke & Simge Küçükyavuz, 2014. "Chance-Constrained Binary Packing Problems," INFORMS Journal on Computing, INFORMS, vol. 26(4), pages 735-747, November.
    13. A. Charnes & W. W. Cooper, 1959. "Chance-Constrained Programming," Management Science, INFORMS, vol. 6(1), pages 73-79, October.
    14. Guglielmo Lulli & Suvrajeet Sen, 2004. "A Branch-and-Price Algorithm for Multistage Stochastic Integer Programming with Application to Stochastic Batch-Sizing Problems," Management Science, INFORMS, vol. 50(6), pages 786-796, June.
    15. Cardoen, Brecht & Demeulemeester, Erik & Beliën, Jeroen, 2010. "Operating room planning and scheduling: A literature review," European Journal of Operational Research, Elsevier, vol. 201(3), pages 921-932, March.
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