Resource planning in the emergency departments: A simulation-based metamodeling approach

Trzeciak, 2003, Emergency department overcrowding in the United States: an emerging threat to patient safety and public health, Emerg. Med. J., 20, 402, 10.1136/emj.20.5.402

Hoot, 2008, Systematic review of emergency department crowding: causes, effects, and solutions, Ann. Emerg. Med., 52, 126, 10.1016/j.annemergmed.2008.03.014

Zilm, 2010, New directions in emergency service operations and planning, J. Ambulatory Care Manage., 33, 296, 10.1097/JAC.0b013e3181f5343d

Nawar, 2007, National hospital ambulatory medical care survey: 2005 emergency department summary, Emergency, 110, 4,000

Horwitz, 2010, United States emergency department performance on wait time and length of visit, Ann. Emerg. Med., 55, 133, 10.1016/j.annemergmed.2009.07.023

Sprivulis, 2006, The association between hospital overcrowding and mortality among patients admitted via Western Australian emergency departments, Med. J. Aust., 184, 208, 10.5694/j.1326-5377.2006.tb00203.x

Amaral, 2014, Improving decision-making and management of hospital resources: an application of the PROMETHEE II method in an emergency department, Oper. Res. Health Care, 3, 1, 10.1016/j.orhc.2013.10.002

Sinreich, 2005, Emergency department operations: the basis for developing a simulation tool, IIE Trans., 37, 233, 10.1080/07408170590899625

Brailsford, 2007, Advances and challenges in healthcare simulation modeling: tutorial, 1436

Paul, 2010, A systematic review of simulation studies investigating emergency department overcrowding, Simulation, 86, 559, 10.1177/0037549709360912

Fu, 2005, Simulation optimization: a review, new developments, and applications, 13

Baesler, 2001, Multi-objective simulation optimization for a cancer treatment center, 1405

Ahmed, 2009, Simulation optimization for an emergency department healthcare unit in Kuwait, Eur. J. Oper. Res., 198, 936, 10.1016/j.ejor.2008.10.025

Cabrera, 2011, Optimization of healthcare emergency departments by agent-based simulation, Procedia Comput. Sci., 4, 1880, 10.1016/j.procs.2011.04.204

Lin, 2012, Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: specific application to determining optimal resource levels in surgical services, Omega

Al-Refaie, 2014, Applying simulation and DEA to improve performance of emergency department in a Jordanian hospital, Simul. Model. Pract. Theory, 41, 59, 10.1016/j.simpat.2013.11.010

Kleijnen, 1975, A comment on Blanning’s “Metamodel for Sensitivity Analysis: The Regression Metamodel in Simulation”, Interfaces, 5, 21, 10.1287/inte.5.3.21

Kleijnen, 2000, A methodology for fitting and validating metamodels in simulation, Eur. J. Oper. Res., 120, 14, 10.1016/S0377-2217(98)00392-0

Barton, 2006, Metamodel-based simulation optimization, Handbooks Oper. Res. Manage. Sci., 13, 535, 10.1016/S0927-0507(06)13018-2

Li, 2010, A systematic comparison of metamodeling techniques for simulation optimization in decision support systems, Appl. Soft Comput., 10, 1257, 10.1016/j.asoc.2009.11.034

Horng, 2013, Evolutionary algorithm assisted by surrogate model in the framework of ordinal optimization and optimal computing budget allocation, Inf. Sci., 10.1016/j.ins.2013.01.024

Marmor, 2009, Toward simulation-based real-time decision-support systems for emergency departments, 2042

Zeltyn, 2011, Simulation-based models of emergency departments: operational, tactical, and strategic staffing, ACM Trans. Model. Comput. Simul. (TOMACS), 21, 24, 10.1145/2000494.2000497

Fernandes, 2005, Five-level triage: a report from the ACEP/ENA five-level triage task force, J. Emerg. Nurs., 31, 39, 10.1016/j.jen.2004.11.002

Kelton, 1998

Kleijnen, 1995, Verification and validation of simulation models, Eur. J. Oper. Res., 82, 145, 10.1016/0377-2217(94)00016-6

Forrester, 2008

Box, 1951, On the experimental attainment of optimum conditions, J. Roy. Stat. Soc. Ser. B (Methodol.), 13, 1, 10.1111/j.2517-6161.1951.tb00067.x

Simpson, 2001, Metamodels for computer-based engineering design: survey and recommendations, Eng. Comput., 17, 129, 10.1007/PL00007198

Hussain, 2002, Metamodeling: radial basis functions, versus polynomials, Eur. J. Oper. Res., 138, 142, 10.1016/S0377-2217(01)00076-5

Rocha, 2009, On the selection of the most adequate radial basis function, Appl. Math. Model., 33, 1573, 10.1016/j.apm.2008.02.008

Chambers, 2002, Process optimization via neural network metamodeling, Int. J. Prod. Econ., 79, 93, 10.1016/S0925-5273(00)00188-2

Altiparmak, 2007, Buffer allocation and performance modeling in asynchronous assembly system operations: an artificial neural network metamodeling approach, Appl. Soft Comput., 7, 946, 10.1016/j.asoc.2006.06.002

Yang, 2010, Neural network metamodeling for cycle time-throughput profiles in manufacturing, Eur. J. Oper. Res., 205, 172, 10.1016/j.ejor.2009.12.026

Alam, 2004, A comparison of experimental designs in the development of a neural network simulation metamodel, Simul. Model. Pract. Theory, 12, 559, 10.1016/j.simpat.2003.10.006

Chen, 2006, A review on design, modeling and applications of computer experiments, IIE Trans., 38, 273, 10.1080/07408170500232495

Kleijnen, 2005, An overview of the design and analysis of simulation experiments for sensitivity analysis, Eur. J. Oper. Res., 164, 287, 10.1016/j.ejor.2004.02.005

Dellino, 2010, Robust optimization in simulation: Taguchi and response surface methodology, Int. J. Prod. Econ., 125, 52, 10.1016/j.ijpe.2009.12.003

Can, 2011, Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems, Comput. Ind. Eng., 61, 447, 10.1016/j.cie.2011.03.012

Meckesheimer, 2002, Computationally inexpensive metamodel assessment strategies, AIAA J., 40, 2053, 10.2514/2.1538

Kleijnen, 2007, Optimization of simulated systems: OptQuest and alternatives, Simul. Model. Pract. Theory, 15, 354, 10.1016/j.simpat.2006.11.001

Miller, 2006, Using RFID technologies to capture simulation data in a hospital emergency department, 1365

Wasserkrug, 2009, InEDvance: advanced IT in support of emergency department management, 86

Kwon, 2014, A real time process management system using RFID data mining, Comput. Ind., 65, 721, 10.1016/j.compind.2014.02.007

Green, 2001, Improving the SIPP approach for staffing service systems that have cyclic demands, Oper. Res., 49, 549, 10.1287/opre.49.4.549.11228