Geographic Virtual Pooling of Hospital Resources: Data-Driven Trade-off Between Waiting and Traveling

References

• Afèche P (2013) Incentive-compatible revenue management in queueing systems: Optimal strategic delay. Manufacturing Service Oper. Management 15(3):423–443.Link, Google Scholar
• Armony M, Roels G, Song H (2017) Pooling queues with discretionary service capacity. History.Google Scholar
• Ayvaz N, Huh WT (2010) Allocation of hospital capacity to multiple types of patients. J. Revenue Pricing Management 9(5):386–398.Crossref, Google Scholar
• Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Networks 5(2):157–166.Crossref, Google Scholar
• Buzacott JA, Shanthikumar JG, Yao DD (1994) Jackson network models of manufacturing systems. Yao DD, ed. Stochastic Modeling and Analysis of Manufacturing Systems, Springer Series in Operations Research (Springer, New York), 1–45.Crossref, Google Scholar
• Canadian Association of Radiologists (2014) Maximum Wait Time Access Targets for Medical Imaging (Canadian Association of Radiologists, Ottawa).Google Scholar
• Chakraborty S, Muthuraman K, Lawley M (2010) Sequential clinical scheduling with patient no-shows and general service time distributions. IIE Trans. 42(5):354–366.Crossref, Google Scholar
• Chvatal V (1979) A greedy heuristic for the set-covering problem. Math. Oper. Res. 4(3):233–235.Link, Google Scholar
• Do H, Shunko M, Lucas M, Novak D (2015) On the pooling of queues: How server behavior affects performance. Preprint, submitted May 15, http://dx.doi.org/10.2139/ssrn.2606071.Google Scholar
• Geng N, Xie X (2016) Optimal dynamic outpatient scheduling for a diagnostic facility with two waiting time targets. IEEE Trans. Automatic Control 61(12):3725–3739.Crossref, Google Scholar
• Hay AM, Valentin EC, Bijlsma RA (2006) Modeling emergency care in hospitals: A paradox-the patient should not drive the process. Proc. 2006 Winter Simulation Conf. (IEEE, Piscataway, NJ), 439–445.Google Scholar
• He QM, Xie J, Zhao X (2012) Priority queue with customer upgrades. Naval Res. Logist. 59(5):362–375.Crossref, Google Scholar
• Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing co-adaptation of feature detectors. Preprint, submitted July 3, https://doi.org/10.48550/arXiv.1207.0580.Google Scholar
• Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput. 9(8):1735–1780.Crossref, Google Scholar
• Hotelling H (1990) Stability in competition. Darnell AC, ed. The Collected Economics Articles of Harold Hotelling (Springer, New York), 50–63.Crossref, Google Scholar
• Jiang Y, Abouee-Mehrizi H, Diao Y (2020) Data-driven analytics to support scheduling of multi-priority multi-class patients with wait time targets. Eur. J. Oper. Res. 281(3):597–611.Crossref, Google Scholar
• Kleinrock L, Finkelstein RP (1967) Time dependent priority queues. Oper. Res. 15(1):104–116.Link, Google Scholar
• Kumar M, Husian M, Upreti N, Gupta D (2010) Genetic algorithm: Review and application. J. Inform. Tech. Knowledge Management 2(2):451–454.Google Scholar
• Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recognition 36(2):451–461.Crossref, Google Scholar
• Lim MK, Mak HY, Shen ZJM (2017) Agility and proximity considerations in supply chain design. Management Sci. 63(4):1026–1041.Link, Google Scholar
• Loch C (1998) Operations management and reengineering. Eur. Management J. 16(3):306–317.Crossref, Google Scholar
• Mandelbaum A, Reiman MI (1998) On pooling in queueing networks. Management Sci. 44(7):971–981.Link, Google Scholar
• Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recognition 33(9):1455–1465.Crossref, Google Scholar
• Min D, Yih Y (2014) Managing a patient waiting list with time-dependent priority and adverse events. RAIRO Oper. Res. 48(1):53–74.Crossref, Google Scholar
• Motiwala SS, Flood CM, Coyte PC, Laporte A (2008) The first ministers’ accord on health renewal and the future of home care in Canada. Preprint, submitted June 20, https://ssrn.com/abstract=1144868.Google Scholar
• Pascanu R, Mikolov T, Bengio Y (2013) On the difficulty of training recurrent neural networks. Internat. Conf. Machine Learn., 1310–1318.Google Scholar
• Patrick J, Puterman ML, Queyranne M (2008) Dynamic multipriority patient scheduling for a diagnostic resource. Oper. Res. 56(6):1507–1525.Link, Google Scholar
• Shunko M, Niederhoff J, Rosokha Y (2018) Humans are not machines: The behavioral impact of queueing design on service time. Management Sci. 64(1):453–473.Link, Google Scholar
• Smith DR, Whitt W (1981) Resource sharing for efficiency in traffic systems. Bell System Tech. J. 60(1):39–55.Crossref, Google Scholar
• Stanford DA, Taylor P, Ziedins I (2014) Waiting time distributions in the accumulating priority queue. Queueing Systems 77(3):297–330.Crossref, Google Scholar
• Van Dijk N, van der Sluis E (2009) Pooling is not the answer. Eur. J. Oper. Res. 197(1):415–421.Crossref, Google Scholar
• Van Mieghem JA (1998) Investment strategies for flexible resources. Management Sci. 44(8):1071–1078.Link, Google Scholar
• Williams RJ, Zipser D (1995) Gradient-based learning algorithms for recurrent networks and their computational complexity. Chauvin Y, Rumelhart DE, eds. Backpropagation: Theory, Architectures, and Applications (Lawrence Erlbaum Associates, Inc., Mahwah, NJ), 433–486.Google Scholar
• Wolsey LA (1982) An analysis of the greedy algorithm for the submodular set covering problem. Combinatorica 2(4):385–393.Crossref, Google Scholar
• Zipkin PH (2000) Foundations of Inventory Management (McGraw-Hill/Irwin, Chicago).Google Scholar