Valuation of hospital resources: an optimization approach using clearing functions

References

• Armony, M., Israelit, S., Mandelbaum, A., Marmor, Y. N., Tseytlin, Y., & Yom-Tov, G. B. (2015). On patient flow in hospitals: A data-based queueing-science perspective. Stochastic Systems, 5(1), 146–194. https://doi.org/10.1287/14-SSY153
   Google Scholar
• Asmundsson, J., Rardin, R. L., Turkseven, C. H., & Uzsoy, R. (2009). Production planning with resources subject to congestion. Naval Research Logistics, 56(2), 142–157. https://doi.org/10.1002/nav.20335
   Web of Science ®Google Scholar
• Asmundsson, J., Rardin, R. L., & Uzsoy, R. (2006). Tractable nonlinear production planning models for semiconductor wafer fabrication facilities. IEEE Transactions on Semiconductor Manufacturing, 19(1), 95–111. https://doi.org/10.1109/TSM.2005.863214
   Web of Science ®Google Scholar
• Aucamp, D. C. (1989). On the use of pseudo shadow prices in a multiperiod formulation of the production planning problem. Journal of the Operational Research Society, 40(12), 1109–1119. https://doi.org/10.1057/jors.1989.192
   Web of Science ®Google Scholar
• Bastos, L. S. L., Marchesi, J. F., Hamacher, S., & Fleck, J. L. (2019). A mixed integer programming approach to the patient admission scheduling problem. European Journal of Operational Research, 273(3), 831–840. https://doi.org/10.1016/j.ejor.2018.09.003
   Web of Science ®Google Scholar
• Bevrani, B., Burdett, R. L., Bhaskar, A., & Yarlagadda, P. K. D. V. (2020). A multicommodity flow model incorporating flow reduction functions. Flexible Services and Manufacturing Journal, 32(3), 693–723. https://doi.org/10.1007/s10696-019-09349-4
   Web of Science ®Google Scholar
• Bhattacharjee, P., & Kumar Ray, P. (2014). Patient flow modelling and performance analysis of healthcare delivery processes in hospitals: A review and reflections. Computers & Industrial Engineering, 78, 299–312. https://doi.org/10.1016/j.cie.2014.04.016
   Web of Science ®Google Scholar
• Blake, J. T., & Carter, M. W. (2002). A goal programming approach to strategic resource allocation in acute care hospitals. European Journal of Operational Research, 140(3), 541–561. https://doi.org/10.1016/S0377-2217(01)00219-3
   Web of Science ®Google Scholar
• Burdett, R. L., & Kozan, E. (2016). A multi-criteria approach for hospital capacity analysis. European Journal of Operational Research, 255(2), 505–521. https://doi.org/10.1016/j.ejor.2016.05.041
   Web of Science ®Google Scholar
• Burdett, R. L., & Kozan, E. (2018). An integrated approach for scheduling health care activities in a hospital. European Journal of Operational Research, 264(2), 756–773. https://doi.org/10.1016/j.ejor.2017.06.051
   Web of Science ®Google Scholar
• Burdett, R. L., Kozan, E., Sinnott, M., Cook, D., & Tian, Y.-C. (2017). A mixed integer linear programing approach to perform hospital capacity assessments. Expert Systems with Applications, 77, 170–188. https://doi.org/10.1016/j.eswa.2017.01.050
   Web of Science ®Google Scholar
• Cardoen, B., Demeulemeester, E., & Beliën, J. (2010). Operating room planning and scheduling: A literature review. European Journal of Operational Research, 201(3), 921–932. https://doi.org/10.1016/j.ejor.2009.04.011
   Web of Science ®Google Scholar
• Carey, M. (1990). Extending and solving a multiperiod congested network flow model. Computers & Operations Research, 17(5), 495–507. https://doi.org/10.1016/0305-0548(90)90054-B
   Web of Science ®Google Scholar
• Carey, M. (1992). Nonconvexity of the dynamic traffic assignment problem. Transportation Research Part B: Methodological, 26(2), 127–133. https://doi.org/10.1016/0191-2615(92)90003-F
   Web of Science ®Google Scholar
• Carey, M., & Bowers, M. (2012). A review of properties of flow–density functions. Transport Reviews, 32(1), 49–73. https://doi.org/10.1080/01441647.2011.608135
   Web of Science ®Google Scholar
• Creemers, S., & Lambrecht, M. (2011). Modeling a hospital queueing network. In R. J. Boucherie & N. M. Van Dijk (Eds.), Queueing networks: A fundamental approach (Chap. 18, International series in operations research and management science, pp. 767–798). Springer.
   Google Scholar
• Curry, G. L., & Feldman, R. M. (2000). Manufacturing systems modelling and analysis. Springer.
   Google Scholar
• Deglise-Hawkinson, J., Helm, J. E., Huschka, T., Kaufman, D. L., & Van Oyen, M. P. (2018). A capacity allocation planning model for integrated care and access management. Production and Operations Management, 27(12), 2270–2290. https://doi.org/10.1111/poms.12941
   PubMed Web of Science ®Google Scholar
• Dexter, F., Blake, J. T., Penning, D. H., & Lubarsky, D. A. (2002). Calculating a potential increase in hospital margin for elective surgery by changing operating room time allocations or increasing nursing staffing to permit completion of more cases: A case study. Anesthesia and Analgesia, 94(1), 138–142. https://doi.org/10.1213/00000539-200201000-00026
   Web of Science ®Google Scholar
• Fügener, A., Hans, E. W., Kolisch, R., Kortbeek, N., & Vanberkel, P. (2014). Master surgery scheduling with consideration of multiple downstream units. European Journal of Operational Research, 239(1), 227–236. https://doi.org/10.1016/j.ejor.2014.05.009
   Web of Science ®Google Scholar
• Gartner, D., & Kolisch, R. (2014). Scheduling the hospital-wide flow of elective patients. European Journal of Operational Research, 233(3), 689–699. https://doi.org/10.1016/j.ejor.2013.08.026
   Web of Science ®Google Scholar
• Gartner, D., & Padman, R. (2020). Flexible hospital-wide elective patient scheduling. Journal of the Operational Research Society, 71(6), 878–892. https://doi.org/10.1080/01605682.2019.1590509
   Web of Science ®Google Scholar
• Gopalswamy, K., & Uzsoy, R. (2019). A data-driven iterative refinement approach for estimating clearing functions from simulation models of production systems. International Journal of Production Research, 57(19), 6013–6030. https://doi.org/10.1080/00207543.2018.1557351
   Web of Science ®Google Scholar
• Gopalswamy, K., & Uzsoy, R. (2021). Conic programming models for production planning with clearing functions: Formulations and duality. European Journal of Operational Research, 292(3), 953–966. https://doi.org/10.1016/j.ejor.2020.11.039
   Web of Science ®Google Scholar
• Green, L. (2004). Capacity planning and management in hospitals. In M. L. Brandeau, F. Sainfort, & W. P. Pierskalla (Eds.), Operations research and health care: A handbook of methods and applications (Chap. 2, International series in operations research & management science, pp. 15–41). Springer.
   Google Scholar
• Hall, R. W. (1991). Queueing methods: For services and manufacturing (Prentice Hall International series in industrial and systems engineering). Prentice Hall.
   Google Scholar
• Hall, R., Belson, D., Murali, P., & Maged, D. (2006). Modeling patient flows through the healthcare system. In R. W. Hall (Ed.), Patient flow: Reducing delay in healthcare delivery (Chap. 1, Vol. 91, International series in operations research & management science, 1–44). Springer.
   Google Scholar
• Hannah, L. A., & Dunson, D. B. (2013). Multivariate convex regression with adaptive partitioning. Journal of Machine Learning Research, 14, 3261–3294.
   Web of Science ®Google Scholar
• Helm, J. E., & Van Oyen, M. P. (2014). Design and optimization methods for elective hospital admissions. Operations Research, 62(6), 1265–1282. https://doi.org/10.1287/opre.2014.1317
   Web of Science ®Google Scholar
• Hof, S., Fügener, A., Schoenfelder, J., & Brunner, J. (2017). Case mix planning in hospitals: A review and future agenda. Health Care Management Science, 20(2), 207–220. https://doi.org/10.1007/s10729-015-9342-2
   Web of Science ®Google Scholar
• Hung, Y.-F., & Leachman, R. C. (1996). A production planning methodology for semiconductor manufacturing based on iterative simulation and linear programming calculations. IEEE Transactions on Semiconductor Manufacturing, 9(2), 257–269. https://doi.org/10.1109/66.492820
   Web of Science ®Google Scholar
• Jansen, B., de Jong, J. J., Roos, C., & Terlaky, T. (1997). Sensitivity analysis in linear programming: Just be careful!. European Journal of Operational Research, 101(1), 15–28. https://doi.org/10.1016/S0377-2217(96)00172-5
   Web of Science ®Google Scholar
• Kacar, N. B., Mönch, L., & Uzsoy, R. (2013). Planning wafer starts using nonlinear clearing functions: A large-scale experiment. IEEE Transactions on Semiconductor Manufacturing, 26(4), 602–612. https://doi.org/10.1109/TSM.2013.2283038
   Web of Science ®Google Scholar
• Kacar, N. B., Mönch, L., & Uzsoy, R. (2016). Modelling cycle times in production planning models for wafer fabrication. IEEE Transactions on Semiconductor Manufacturing, 29(2), 153–167. https://doi.org/10.1109/TSM.2016.2546314
   Web of Science ®Google Scholar
• Kacar, N. B., & Uzsoy, R. (2014). A comparison of multiple linear regression approaches for fitting clearing functions to empirical data. International Journal of Production Research, 52(11), 3164–3184. https://doi.org/10.1080/00207543.2013.864052
   Web of Science ®Google Scholar
• Kacar, N. B., & Uzsoy, R. (2015). Estimating clearing functions for production resources using simulation optimization. IEEE Transactions on Automation Science and Engineering, 12(2), 539–552. https://doi.org/10.1109/TASE.2014.2303316
   Web of Science ®Google Scholar
• Kacar, N. B., Mönch, L., & Uzsoy, R. (2018). Problem reduction approaches for production planning using clearing functions. 2018 IEEE 14th International Conference on Automation Science and Engineering, pp. 931–938.
   Google Scholar
• Karmarkar, U. S. (1989). Capacity loading and release planning with work-in-progress (WIP) and leadtimes. Journal of Manufacturing and Operations Management, 2, 105–123.
   Google Scholar
• Kefeli, A., & Uzsoy, R. (2016). Identifying potential bottlenecks in production systems using dual prices from a mathematical programming model. International Journal of Production Research, 54(7), 2000–2018. https://doi.org/10.1080/00207543.2015.1076182
   Web of Science ®Google Scholar
• Kefeli, A., Uzsoy, R., Fathi, Y., & Kay, M. (2011). Using a mathematical programming model to examine the marginal price of capacitated resources. International Journal of Production Economics, 131(1), 383–391. https://doi.org/10.1016/j.ijpe.2010.03.018
   Web of Science ®Google Scholar
• Kim, S., & Uzsoy, R. (2008). Integrated planning of production and engineering process improvement. IEEE Transactions on Semiconductor Manufacturing, 21(3), 390–398. https://doi.org/10.1109/TSM.2008.2001215
   Web of Science ®Google Scholar
• Kingman, J. F. C. (1961). The single server queue in heavy traffic. Mathematical Proceedings of the Cambridge Philosophical Society, 57(4), 902–904. https://doi.org/10.1017/S0305004100036094
   Google Scholar
• Koltai, T., & Tatay, V. (2011). A practical approach to sensitivity analysis in linear programming under degeneracy for management decision making. International Journal of Production Economics, 131(1), 392–398. https://doi.org/10.1016/j.ijpe.2010.04.037
   Google Scholar
• Koltai, T., & Terlaky, T. (2000). The difference between the managerial and mathematical interpretation of sensitivity analysis results in linear programming. International Journal of Production Economics, 65(3), 257–274. https://doi.org/10.1016/S0925-5273(99)00036-5
   Web of Science ®Google Scholar
• Ma, G., & Demeulemeester, E. (2013). A multilevel integrative approach to hospital case mix and capacity planning. Computers & Operations Research, 40(9), 2198–2207. https://doi.org/10.1016/j.cor.2012.01.013
   Web of Science ®Google Scholar
• Missbauer, H. (2002). Aggregate order release planning for time-varying demand. International Journal of Production Research, 40(3), 699–718. https://doi.org/10.1080/00207540110090939
   Web of Science ®Google Scholar
• Missbauer, H., & Uzsoy, R. (2020). Production planning with capacitated resources and congestion. Springer.
   Google Scholar
• Moosavi, A., & Ebrahimnejad, S. (2018). Scheduling of elective patients considering upstream and downstream units and emergency demand using robust optimization. Computers & Industrial Engineering, 120, 216–233. https://doi.org/10.1016/j.cie.2018.04.047
   Web of Science ®Google Scholar
• Morikawa, K., Takahashi, K., & Hirotani, D. (2018). Performance evaluation of candidate appointment schedules using clearing functions. Journal of Intelligent Manufacturing, 29(3), 509–518. https://doi.org/10.1007/s10845-015-1134-5
   Web of Science ®Google Scholar
• Peeta, S., & Ziliaskopoulos, A. K. (2001). Foundations of dynamic traffic assignment: The past, the present and the future. Networks and Spatial Economics, 1(3/4), 233–265. https://doi.org/10.1023/A:1012827724856
   Google Scholar
• Purgstaller, P., & Missbauer, H. (2012). Rule-based vs. optimization-based order release in workload control: A simulation study of an MTO manufacturer. International Journal of Production Economics, 140(2), 670–680. https://doi.org/10.1016/j.ijpe.2011.09.012
   Web of Science ®Google Scholar
• Roth, A. V., & Dierdonck, R. (1995). Hospital resource planning: Concepts, feasibility, and framework. Production and Operations Management, 4(1), 2–29. https://doi.org/10.1111/j.1937-5956.1995.tb00038.x
   Google Scholar
• Sengul, I., Ivy, J. S., Uzsoy, R., & Wilson, J. R. (2016). Modeling for equitable and effective food distribution under capacity constraints. IIE Transactions on Operations Engineering, 48(3), 252–266.
   Google Scholar
• Srinivasan, A., Carey, M., & Morton, T. E. (1988). Resource pricing and aggregate scheduling in manufacturing systems. Working Paper 88-89-58, Carnegie Mellon University, Graduate School of Industrial Administration, Pittsburgh, PA.
   Google Scholar
• Thürer, M., Stevenson, M., & Silva, C. (2011). Three decades of workload control research: A systematic review of the literature. International Journal of Production Research, 49(23), 6905–6935. https://doi.org/10.1080/00207543.2010.519000
   Web of Science ®Google Scholar
• Vissers, J. M. H., Adan, I. J. B. F., & Bekkers, J. A. (2005). Patient mix optimization in tactical cardiothoracic surgery planning: A case study. IMA Journal of Management Mathematics, 16(3), 281–304. https://doi.org/10.1093/imaman/dpi023
   Google Scholar
• Ziarnetzky, T., Mönch, L., & Uzsoy, R. (2018). Rolling horizon production planning for wafer fabs with chance constraints and forecast evolution. International Journal of Production Research, 56(18), 6112–6134. https://doi.org/10.1080/00207543.2018.1478461
   Web of Science ®Google Scholar
• Ziarnetzky, T., Mönch, L., & Uzsoy, R. (2020). Simulation-based performance assessment of production planning formulations with safety stock and forecast evolution using a large-scale wafer fab model. IEEE Transactions on Semiconductor Manufacturing, 33(1), 1–20. https://doi.org/10.1109/TSM.2019.2958526
   Web of Science ®Google Scholar