Master surgical scheduling problem with multiple criteria and robust estimation

Document Type : Article

Authors

Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran 15875/4416, Iran

Abstract

In this research the master surgical scheduling (MSS) problem at the tactical level of hospital planning and scheduling is studied. Before constructing the MSS, a strategic level problem, i.e. case mix planning problem (CMPP), shall be solved to allocate the capacity of operating room (OR) to each surgical specialty. In order to make an effective coordination between CMPP and MSS, the results obtained from solving the CMPP is used as an input for the respective MSS. In the MSS, frequently performed elective surgeries are planned in a cyclic manner for a pre-defined planning period. As a part of the planning process, it is required to level downstream limited resources such as intensive care unit (ICU) and ward beds with patient flow. In this study, a mathematical model is developed to construct an MSS. The proposed model is based on a lexicographic goal programming approach which is aimed at minimizing the OR spare time while considering the results of the CMPP. In this paper, data required to solve MSS, is collected from a medium-sized Iranian hospital. Hence, a robust estimation method is applied to reduce the effect of outliers in the decision making process. The results testify the performance of the proposed method against the solution put in practice in the hospital.

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References

References:
1. Hof, S., Fugener, A., Schoenfelder, J., and Brunner, J.O. "Case mix planning in hospitals: a review and future agenda", Health Care Management Science, 20(2), pp. 207-220 (2017).
2. Hartman, M., Martin, A.B., Benson, J., and Catlin, A. National Health Expenditure Accounts Team. "National health spending in 2011: overall growth remains low, but some payers and services show signs of acceleration", Health Affairs, 32(1), pp. 87-99 (2013).
3. Denton, B., Viapiano, J., and Vogl, A. "Optimization of surgery sequencing and scheduling decisions under uncertainty", Health Care Management Science, 10(1), pp. 13-24 (2007).
4. Healthcare Financial Management Association "Achieving operating room efficiency through process integration", Healthcare Financial Management: Journal of the Healthcare Financial Management Association, 57(3), suppl-1 (2003).
5. Yahia, Z., Eltawil, A.B., and Harraz, N.A. "The operating room case-mix problem under uncertainty and nurses capacity constraints", Health Care Management Science, pp. 1-12 (2015).
6. Patterson, P. "What makes a well-oiled scheduling system", OR Manager, 12(9), pp. 19-23 (1996).
7. Dexter, F., Traub, R.D., and Macario, A. "How to release allocated operating room time to increase efficiency: predicting which surgical service will have the most underutilized operating room time", Anesthesia & Analgesia, 96(2), pp. 507-512 (2003).
8. Vacanti, C., Segal, S., Sikka, P., and Urman, R. (Eds.), Essential Clinical Anesthesia, Cambridge University Press (2011).
9. Roth, A.V. and Van Dierdonck, R. "Hospital resource planning: concepts, feasibility, and framework", Production and Operations Management, 4(1), pp. 2-29 (1995).
10. Vissers, J.M., Bertrand, J.W.M., and De Vries, G. "A  ramework for production control in health care organizations", roduction Planning & Control, 12(6), pp. 591-604 (2001).
11. Hans, E.W., Van Houdenhoven, M., and Hulshof, P.J. "A framework for healthcare planning and control", In Handbook of Healthcare System Scheduling, pp. 303- 320, Springer US (2012).
12. Belien, J., and Demeulemeester, E. "Building cyclic master surgery schedules with leveled resulting bed occupancy", European Journal of Operational Research, 176(2), pp. 1185-1204 (2007).
13. Samudra, M., Van Riet, C., Demeulemeester, E., Cardoen, B., Vansteenkiste, N., and Rademakers, F.E. "Scheduling operating rooms: achievements, challenges and pitfalls", Journal of Scheduling, 19(5), pp. 493-525 (2016).
14. Belien, J., Demeulemeester, E., and Cardoen, B. "A decision support system for cyclic master surgery scheduling with multiple objectives", Journal of Scheduling, 12(2), pp. 147-161 (2009).
15. Van Oostrum, J.M., Van Houdenhoven, M., Hurink, J.L., Hans, E.W., Wullink, G., and Kazemier, G. "A master surgical scheduling approach for cyclic scheduling in operating room departments", OR Spectrum, 30(2), pp. 355-374 (2008).
16. Vissers, J.M.H., Adan, I.J., and Bekkers, J.A. "Patient mix optimization in tactical cardiothoracic surgery planning: a case study", IMA Journal of Management Mathematics, 16(3), pp. 281-304 (2005).
17. Holte, M. and Mannino, C. "The implementor/ adversarial algorithm for cyclic and robust scheduling problems in health-care", Department of Computer and System Sciences Antonio Ruberti Technical Reports, 3(3), 19 pages (2011).
18. Fugener, A., Hans, E.W., Kolisch, R., Kortbeek, N., and Vanberkel, P.T. "Master surgery scheduling with consideration of multiple downstream units", European Journal of Operational Research, 239(1), pp. 227-236 (2014).
19. Dexter, F., Ledolter, J., and Wachtel, R.E. "Tactical decision making for selective expansion of operating room resources incorporating financial criteria and uncertainty in subspecialties' future wor loads", Anesthesia & Analgesia, 100(5), pp. 1425-1432 (2005).
20. Kharraja, S., Albert, P., and Chaabane, S. "Block scheduling: Toward a master surgical schedule", In Service Systems and Service Management, 2006 International Conference on, 1, pp. 429-435, IEEE (Oct. 2006).
21. Agnetis, A., Coppi, A., Corsini, M., Dellino, G., Meloni, C., and Pranzo, M. "Long term evaluation of operating theater planning policies", Operations Research for Health Care, 1(4), pp. 95-104 (2012).
22. Cappanera, P., Visintin, F., and Banditori, C. "Comparing resource balancing criteria in master surgical scheduling: A combined optimisation-simulation approach", International Journal of Production Economics, 158, pp. 179-196 (2014).
23. Banditori, C., Cappanera, P., and Visintin, F. "A combined optimization-simulation approach to the master surgical scheduling problem", IMA Journal of Management Mathematics, dps033, 24(2), pp. 155-187 (2013).
24. Cappanera, P., Visintin, F., and Banditori, C. "A goalprogramming approach to the master surgical scheduling problem", In Health Care Systems Engineering for Scientists and Practitioners, pp. 155-166, Springer International Publishing (2016).
25. Cappanera, P., Visintin, F., and Banditori, C. "Addressing conflicting stakeholders' priorities in surgical scheduling by goal programming", Flexible Services and Manufacturing Journal, 30(1-2), pp. 252-271 (2018).
26. Holte, M. and Mannino, C. "The implementor/ adversary algorithm for the cyclic and robust scheduling problem in health-care", European Journal of Operational Research, 226(3), pp. 551-559 (2013).
27. Visintin, F., Cappanera, P., and Banditori, C. "Evaluating the impact of flexible practices on the master surgical scheduling process: an empirical analysis", Flexible Services and Manufacturing Journal, 28(1-2), pp. 182-205 (2016).
28. Rowse, E., Lewis, R., Harper, P., and Thompson, J. "Set partitioning methods for scheduling: an application to operating theatres", Simposio Brasileiro de Pesquisa Operacional (2014).
29. Fugener, A. "An integrated strategic and tactical master surgery scheduling approach with stochastic resource demand", Journal of Business Logistics, 36(4), pp. 374-387 (2015).
30. Van Oostrum, J.M., Bredenhoff, E., and Hans, E.W. "Suitability and managerial implications of a master surgical scheduling approach", Annals of Operations Research, 178(1), pp. 91-104 (2010).
31. Visintin, F., Cappanera, P., Banditori, C., and Danese, P. "Development and implementation of an operating room scheduling tool: an action research study", Production Planning & Control, 28(9), pp. 758-775 (2017).
32. Mandic, K., Delibasic, B., Knezevic, S., and Benkovic, S. "Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods", Economic Modelling, 43, pp. 30-37 (2014).
33. Hwang, C.L. and Yoon, K., Multiple Attribute Decision Making: Methods and Applications, New York: Springer-Verlag (1981).
34. Grubbs, F.E. "Procedures for detecting outlying observations in samples", Technometrics, 11(1), pp. 1-21 (1969).
35. Maddala, G.S. and Lahiri, K., Introduction to Econometrics, 2, New York: Macmillan (1992).
36. Huber, P.J., Robust Statistics, pp. 1248-1251, Springer, Berlin, Heidelberg (2011).
37. Freitas, A., Silva-Costa, T., Lopes, F., Garcia-Lema, I., Teixeira-Pinto, A., Brazdil, P., and Costa-Pereira, A. "Factors influencing hospital high length of stay outliers", BMC Health Services Research, 12(1), p. 1 (2012).
38. Cots, F., Elvira, D., Castells, X., and Dalmau, E. "Medicare's DRG-weights in a European environment: the Spanish experience", Health Policy, 51(1), pp. 31- 47 (2000).
39. Pirson, M., Dramaix, M., Leclercq, P., and Jackson, T. "Analysis of cost outliers within APR-DRGs in a Belgian general hospital: two complementary approaches", Health Policy, 76(1), pp. 13-25 (2006).
40. Das, P., and Mandal, D., Statistical Outlier Detection in Large Multivariate Datasets, pp. 1-9. acsu.buffalo.edu.
41. Zimek, A., Schubert, E., and Kriegel, H.P. "A survey on unsupervised outlier detection in high-dimensional numerical data", Statistical Analysis and Data Mining, 5(5), pp. 363-387 (2012).
42. Ruckstuhl, A.F. and Welsh, A.H. "Robust fitting of the binomial model", Annals of Statistics, 29(4), pp. 1117-1136 (2001).
Scientia Iranica
Volume 26, Issue 1
Transactions on Industrial Engineering (E)
January and February 2019
Pages 486-502

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