C. Cassandras and S. Lafortune, Introduction to discrete event systems, 1999.

G. Fleury, P. Lacomme, and A. Tanguy, Simulation à événements discrets, Eyrolles, 2007.

J. Dréo, A. Pétrowski, P. Siarry, and E. Taillard, Métaheuristiques pour l'optimisation dicile, Eyrolles, 2005.

W. T. Lin, Y. C. Wu, J. S. Zheng, and M. Y. Chen, Analysis by data mining in the emergency medicine triage database at a Taiwanese regional hospital, Expert Systems with Applications, vol.38, issue.9, pp.11-07811, 2011.

N. Lavra£, Selected techniques for data mining in medicine, Articial Intelligence in Medicine, vol.16, issue.1, p.323, 1999.

M. J. Crawley, Analysis of Variance, The R Book, p.449488, 2007.

R. Agrawal, T. Imieli«ski, and A. Swami, Mining Association Rules Between Sets of Items in Large Databases, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, ser. SIGMOD '93, p.207216, 1993.

J. Y. Lettvin, H. R. Maturana, W. S. Mcculloch, and W. H. Pitts, What the Frog's

, Eye Tells the Frog's Brain, Proceedings of the IRE, vol.47, issue.11, p.19401951, 1959.

D. W. Aha, D. Kibler, and M. K. Albert, Instance-based learning algorithms, Machine Learning, vol.6, p.3766, 1991.

J. Holland, Genetic Algorithms and the Optimal Allocation of Trials, SIAM Journal on Computing, vol.2, issue.2, p.88105, 1973.

G. Vassilacopoulos, Allocating Doctors to Shifts in an Accident and Emergency Department, The Journal of the Operational Research Society, vol.36, issue.6, p.517523, 1985.

A. M. Bagirov, L. M. Churilov-;-p, D. Sloot, and A. V. Abramson, An Optimization-Based Approach to Patient Grouping for Acute Healthcare in Australia, Computational Science ICCS 2003, ser. Lecture Notes in Computer Science

Y. E. Bogdanov, J. J. Gorbachev, and . Dongarra, , p.2029, 2003.

I. K. Altinel and E. Ulas, Simulation modeling for emergency bed requirement planning, Annals of Operations Research, vol.67, issue.1, p.183210, 1996.

E. Cabrera, E. Luque, M. Taboada, F. Epelde, and M. Iglesias, ABMS optimization for emergency departments, Simulation Conference (WSC), Proceedings of the 2012 Winter, p.112, 2012.

L. Gilson, Trust and the development of health care as a social institution, Social Science & Medicine, vol.56, issue.7, p.14531468, 2003.

A. Banerjee, A. Deaton, and E. Duo, Health, Health care, and Economic Developement, The American economic review, vol.94, issue.2, p.326330, 2004.

V. R. Kutty, Historical analysis of the development of health care facilities in Kerala State, Health Policy and Planning, vol.15, issue.1, p.103109, 2000.

A. Boyle, K. Beniuk, I. Higginson, and P. Atkinson, Emergency Department Crowding : Time for Interventions and Policy Evaluations, Emergency Medicine International, vol.2012, p.838610, 2012.

D. Yu, R. C. Blocker, M. Y. Sir, M. S. Hallbeck, T. R. Hellmich et al.,

K. S. Nestler and . Pasupathy, Intelligent Emergency Department : Validation of Sociometers to Study Workload, Journal of Medical Systems, vol.40, issue.3, p.53, 2016.

, oru ca.com, Panorama Urgences, 2013.

R. Derlet, J. Richards, and R. Kravitz, Frequent overcrowding in U.S. emergency departments, Academic Emergency Medicine : Ocial Journal of the Society for Academic Emergency Medicine, vol.8, issue.2, p.151155, 2001.

M. Foley, N. Kifaieh, and W. K. Mallon, Financial Impact of Emergency Department Crowding, Western Journal of Emergency Medicine, vol.12, issue.2, p.192197, 2011.

B. C. Sun, R. Y. Hsia, R. E. Weiss, D. Zingmond, L. Liang et al., Eect of emergency department crowding on outcomes of admitted patients, Annals of Emergency Medicine, vol.61, issue.6, pp.605611-605617, 2013.

I. Ajmi, H. Zgaya, and S. Hammadi, Optimized Workow for the Healthcare Logistic : Case of the Pediatric Emergency Department, 7th International Conference on Practical Applications of Computational Biology & Bioinformatics, ser. Advances in Intelligent Systems and Computing, p.7784, 2013.

H. Y. Atallah and E. K. Lee, Modeling and Optimizing Emergency Department Workow

M. Gendreau, J. Ferland, B. Gendron, N. Hail, B. Jaumard et al., Physician Scheduling in Emergency Rooms, in Practice and Theory of Automated Timetabling VI, ser. Lecture Notes in Computer Science, p.5366, 2007.

N. Coskun and R. Erol, An Optimization Model for Locating and Sizing Emergency Medical Service Stations, Journal of Medical Systems, vol.34, issue.1, p.4349, 2010.

B. Y. Lin, C. C. Hsu, M. Chao, S. Luh, S. Hung et al., Physician and Nurse Job Climates in Hospital-Based Emergency Departments in Taiwan : Management and Implications, Journal of Medical Systems, vol.32, issue.4, p.269281, 2008.

L. Luo, Y. Luo, Y. You, Y. Cheng, Y. Shi et al., A MIP Model for Rolling Horizon Surgery Scheduling, Journal of Medical Systems, vol.40, issue.5, p.127, 2016.

M. Cooke, S. Wilson, J. Halsall, and A. Roalfe, Total time in English accident and emergency departments is related to bed occupancy, Emergency Medicine Journal : EMJ, vol.21, issue.5, p.575576, 2004.

M. J. Schull, M. M. Mamdani, and J. Fang, Inuenza and emergency department utilization by elders, Academic Emergency Medicine : Ocial Journal of the Society for Academic Emergency Medicine, vol.12, issue.4, 2005.

J. H. Han, C. Zhou, D. J. France, S. Zhong, I. Jones et al., The eect of emergency department expansion on emergency department overcrowding, Academic Emergency Medicine : Ocial Journal of the Society for Academic Emergency Medicine, vol.14, issue.4, p.338343, 2007.

J. G. De-gooijer and R. J. Hyndman, 25 years of time series forecasting, International Journal of Forecasting, vol.22, issue.3, p.443473, 2006.

G. U. Yule, On a Method of Investigating Periodicities in Disturbed Series, with Special Reference to Wolfer's Sunspot Numbers, Philosophical Transactions of the Royal Society of London A : Mathematical, Physical and Engineering Sciences, vol.226, p.267298, 1927.

G. Walker, On Periodicity in Series of Related Terms, Proceedings of the Royal Society of London A : Mathematical, Physical and Engineering Sciences, vol.131, issue.818, p.518532, 1931.

G. E. Box and G. M. Jenkins, Time Series Analysis, Forecasting and Control, Journal of the Royal Statistical Society. Series A (General), vol.134, issue.3, p.450, 1971.

D. F. Findley, B. C. Monsell, W. R. Bell, M. C. Otto, and B. Chen, New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program, Journal of Business & Economic Statistics, vol.16, issue.2, p.127, 1998.

R. G. Brown, Statistical forecasting for inventory control, 1959.

C. C. Holt, Forecasting seasonals and trends by exponentially weighted moving averages, International Journal of Forecasting, vol.20, issue.1, p.510, 2004.

P. R. Winters, Forecasting Sales by Exponentially Weighted Moving Averages, Management Science, vol.6, issue.3, p.324342, 1960.

R. E. , Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks, Computers & Industrial Engineering, vol.54, issue.4, p.903917, 2008.

J. Shahrabi, E. Hadavandi, and S. Asadi, Developing a hybrid intelligent model for forecasting problems : Case study of tourism demand time series, Knowledge-Based Systems, vol.43, p.112122, 2013.

S. F. Crone, M. Hibon, and K. Nikolopoulos, Corrigendum to Advances in forecasting with neural networks ? Empirical evidence from the NN3 competition on time series prediction, International Journal of Forecasting, vol.27, issue.4, p.1138, 2011.

S. M. Clarke, J. H. Griebsch, and T. W. Simpson, Analysis of Support Vector Regression for Approximation of Complex Engineering Analyses, Journal of Mechanical Design, vol.127, issue.6, p.10771087, 2004.

W. Hong, Electric load forecasting by support vector model, Applied Mathematical Modelling, vol.33, issue.5, p.24442454, 2009.

G. K. Tso and K. K. Yau, Predicting electricity energy consumption : A comparison of regression analysis, decision tree and neural networks, Energy, vol.32, p.17611768, 2007.

L. Yang, J. C. Lam, J. Liu, and C. L. Tsang, Building energy simulation using multi-years and typical meteorological years in dierent climates, Energy Conversion and Management, vol.49, issue.1, p.113124, 2008.

J. C. Lam, K. K. Wan, D. Liu, and C. L. Tsang, Multiple regression models for energy use in air-conditioned oce buildings in dierent climates, Energy Conversion and Management, vol.51, issue.12, p.26922697, 2010.

U. D. Caprio, R. Genesio, S. Pozzi, and A. Vicino, Short term load forecasting in electric power systems : A comparison of ARMA models and extended wiener ltering, Journal of Forecasting, vol.2, issue.1, p.5976, 1983.

J. D. Cummins and G. L. Griepentrog, Forecasting automobile insurance paid claim costs using econometric and ARIMA models, International Journal of Forecasting, vol.1, issue.3, p.203215, 1985.

S. E. Hein and R. E. Spudeck, Forecasting the daily federal funds rate, International Journal of Forecasting, vol.4, issue.4, p.581591, 1988.

P. J. Dhrymes and S. C. Peristiani, A comparison of the forecasting performance of WEFA and ARIMA time series methods, International Journal of Forecasting, vol.4, issue.1, p.81101, 1988.

M. D. Geurts and J. Kelly, Forecasting retail sales using alternative models, International Journal of Forecasting, vol.2, issue.3, p.261272, 1986.

P. Grambsch and W. A. Stahel, Forecasting demand for special telephone services, International Journal of Forecasting, vol.6, issue.1, 1990.

P. Paumer, Forecasting US population totals with the Box-Jenkins approach, International Journal of Forecasting, vol.8, issue.3, p.329338, 1992.

J. Preez and S. F. Witt, Univariate versus multivariate time series forecasting : an application to international tourism demand, International Journal of Forecasting, vol.19, issue.3, p.435451, 2003.

A. P. Layton, L. V. Defris, and B. Zehnwirth, An international comparison of economic leading indicators of telecommunications trac, International Journal of Forecasting, vol.2, issue.4, p.413425, 1986.

L. Bianchi, J. Jarrett, and R. Hanumara, Improving forecasting for telemarketing centers by ARIMA modeling with intervention, International Journal of Forecasting, vol.14, issue.4, p.497504, 1998.

R. M. Heuts and J. H. Bronckers, Forecasting the Dutch heavy truck market, International Journal of Forecasting, vol.4, issue.1, p.5779, 1988.

S. C. Hillmer, D. F. Larcker, and D. A. Schroeder, Forecasting accounting data : A multiple time-series analysis, Journal of Forecasting, vol.2, issue.4, p.389404, 1983.

W. T. Lin, Modeling and forecasting hospital patient movements : Univariate and multiple time series approaches, International Journal of Forecasting, vol.5, issue.2, 1989.

J. M. Bates and C. W. Granger, The Combination of Forecasts, OR, vol.20, p.451468, 1969.

P. Newbold, C. Granger, and J. R. , Stat. Soc. Ser. A-G, vol.137, issue.2, p.131165, 1974.

R. Winkler and S. Makridakis, The combination of forecasts, Journal of the Royal Statistical Society, Series A, vol.146, issue.2, p.150157, 1983.

R. T. Clemen, Combining forecasts : A review and annotated bibliography, International Journal of Forecasting, vol.5, issue.4, p.559583, 1989.

D. Bunn, Statistical eciency in the linear combination of forecasts, International Journal of Forecasting, vol.1, issue.2, p.151163, 1985.

C. Granger and R. Ramanathan, Improved methods of combining forecasts, Journal of Forecasting, vol.3, issue.2, p.197204, 1984.

C. Aksu and S. I. Gunter, An empirical analysis of the accuracy of SA, OLS, ERLS and NRLS combination forecasts, International Journal of Forecasting, vol.8, issue.1, p.2743, 1992.

S. Gunter, Nonnegativity restricted least squares combinations, International Journal of Forecasting, vol.8, issue.1, p.4559, 1992.

C. M. Miller, R. T. Clemen, and R. L. Winkler, The eect of nonstationarity on combined forecasts, International Journal of Forecasting, vol.7, issue.4, p.515529, 1992.

L. M. De-menezes and D. W. Bunn, The persistence of specication problems in the distribution of combined forecast errors, International Journal of Forecasting, vol.14, issue.3, p.415426, 1998.

J. W. Taylor and D. W. Bunn, Investigating improvements in the accuracy of prediction intervals for combinations of forecasts : A simulation study, International Journal of Forecasting, vol.15, issue.3, p.325339, 1999.

M. Hibon and T. Evgeniou, To combine or not to combine : selecting among forecasts and their combinations, International Journal of Forecasting, vol.21, issue.1, p.1524, 2005.

P. C. Milner, Forecasting the demand on accident and emergency departments in health districts in the Trent region, Statistics in Medicine, vol.7, issue.10, p.10611072, 1988.

F. Kadri, F. Harrou, S. Chaabane, and C. Tahon, Time Series Modelling and Forecasting of Emergency Department Overcrowding, Journal of Medical Systems, vol.38, issue.9, p.120, 2014.

Y. Sun, B. H. Heng, Y. T. Seow, and E. Seow, Forecasting daily attendances at an emergency department to aid resource planning, BMC Emergency Medicine, vol.9, issue.1, p.19, 2009.

S. A. Jones, M. P. Joy, and J. Pearson, Forecasting Demand of Emergency Care, Health Care Management Science, vol.5, issue.4, p.297305, 2002.

S. S. Jones, R. S. Evans, T. L. Allen, A. Thomas, P. J. Haug et al.,

. Snow, A multivariate time series approach to modeling and forecasting demand in the emergency department, Journal of Biomedical Informatics, vol.42, issue.1, p.123139, 2009.

H. Shi, J. Tsai, W. Ho, and K. Lee, Autoregressive integrated moving average model for long-term prediction of emergency department revenue and visitor volume, 2011 International Conference on Machine Learning and Cybernetics (ICMLC), vol.3, p.979982, 2011.

A. Ekström, L. Kurland, N. Farrokhnia, M. Castrén, and M. Nordberg, Forecasting Emergency Department Visits Using Internet Data, Annals of Emergency Medicine

J. Boyle, M. Wallis, M. Jessup, J. Crilly, J. Lind et al., Regression forecasting of patient admission data, 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, p.38193822, 2008.

J. Bergs, P. Heerinckx, and S. Verelst, Knowing what to expect, forecasting monthly emergency department visits : A time-series analysis, International Emergency Nursing, vol.22, issue.2, p.112115, 2014.

J. Stout, W. A. , and B. Tawney, An Excel forecasting model to aid in decision making that aects hospital resource/bed utilization -hospital capability to admit emergency room patients, 2005 IEEE Systems and Information Engineering Design Symposium, p.222228, 2005.

B. Mielczarek, Estimating future demand for hospital emergency services at the regional level, Simulation Conference (WSC), p.23862397, 2013.

G. Bouleux, E. Marcon, and O. Mory, Early Index for Detection of Pediatric Emergency Department Crowding, IEEE Journal of Biomedical and Health Informatics, issue.99, p.11, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01359977

D. C. Medina, S. E. Findley, B. Guindo, and S. Doumbia, Forecasting Non-Stationary Diarrhea, Acute Respiratory Infection, and Malaria Time-Series in Niono, PLoS ONE, vol.2, issue.11, 2007.

R. J. Hyndman, A. B. Koehler, R. D. Snyder, and S. Grose, A state space framework for automatic forecasting using exponential smoothing methods, International Journal of Forecasting, vol.18, issue.3, p.439454, 2002.

R. Champion, L. D. Kinsman, G. A. Lee, K. A. Masman, E. A. May et al., Forecasting emergency department presentations, Australian Health Review : A Publication of the, Australian Hospital Association, vol.31, issue.1, p.8390, 2007.

P. Aboagye-sarfo, Q. Mai, F. M. Sanlippo, D. B. Preen, L. M. Stewart et al., A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia, Journal of Biomedical Informatics, vol.57, p.6273, 2015.

M. Jalalpour, Y. Gel, and S. Levin, Forecasting demand for health services : Development of a publicly available toolbox, Operations Research for Health Care, vol.5, p.19, 2015.

N. R. Sanders and F. Theory, Wiley Encyclopedia of Electrical and Electronics Engineering, 2001.

, Introduction to Time Series and Forecasting, ser. Springer Texts in Statistics, 2002.

R. B. Cleveland, W. S. Cleveland, and I. Terpenning, STL : A seasonal-trend decomposition procedure based on loess, Journal of Ocial Statistics, vol.6, issue.1, p.3, 1990.

A. Yalaoui, H. Chehade, F. Yalaoui, and L. Amodeo, Optimization of logistics, 2012.
URL : https://hal.archives-ouvertes.fr/hal-02500787

C. Chateld, The analysis of time series : an introduction, 2016.

C. Chateld, Calculating interval forecasts, Journal of Business and Economic Statistics, vol.11, issue.2, p.121135, 1993.

H. Akaike, A new look at the statistical model identication, IEEE transactions on automatic control, vol.19, issue.6, p.716723, 1974.

M. Rosenblatt, Y. Roll, and V. Zyser, A combined optimization and simulation approach for designing automated storage/retrieval systems, IIE Transactions, vol.25, p.2550, 1993.

E. Williams and I. Ahitov, Scheduling analysis using discrete event simulation, Proceedings of the 29th Annual Simulation Symposium, p.148154, 1996.

H. , Using evolutionary algorithms and simulation for the optimization of manufacturing systems, IIE Transactions, vol.29, issue.3, p.181190, 1997.

R. Marasini and N. Dawood, Simulation modelling and optimization of stockyard layouts for precast concrete products, Proceedings of the 34th Winter Simulation conference, p.17311736, 2002.

S. Gerner, A. Kobeissi, B. David, Z. Binder, and B. Descotes-genon, Integrated approach for disassembly processes generation and recycling evaluation of an end-of-life product, International Journal of Production Research, vol.43, issue.1, p.195222, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00069618

P. Sahlin, L. Eriksson, P. Grozman, H. Johnsson, and A. Shapovalov, Whole building simulation with symbolic dae equations and general purpose solvers, Building and Environment, vol.39, p.949958, 2004.

C. Lutz, K. R. Davis, and M. Sun, Determining buer location and size in production lines using tabu search, European Journal of Operational Research, vol.106, pp.301-316, 1998.

A. Yalcin and R. Nambella, An object oriented simulation framework for real-time control of automated exible manufacturing systems, Computers and Industrial Engineering, vol.48, p.111127, 2005.

H. Chehade, L. Amodeo, and F. Yalaoui, Simulation based optimization of a printing workshop using NSGA-II, CD-ROM Proceedings). Hammamet, Tunisia : Métaheuristiques, 2006.

Y. Hani, H. Chehade, L. Amodeo, and F. Yalaoui, Algorithme génétique pour l'optimisation multiobjectif dans un atelier de maintenance ferroviaire, Proceedings du 7eme congrès de la société française de recherche opérationnelle et d'aide à la décision ROADEF, 2006.

A. Iannoni and R. Morabito, A discrete simulation analysis of a logistics supply system, Transportation Research Part E, vol.42, p.191210, 2006.

J. Proth and N. Sauer, Scheduling of piecewise constant product ows : A petri net approach, European Journal of Operational Research, vol.106, issue.1, pp.45-56, 1998.

A. M. Law, W. D. Kelton, and W. D. Kelton, Simulation modeling and analysis, vol.2, 1991.

T. Eldabi, R. Paul, and T. Young, Simulation modelling in healthcare : reviewing legacies and investigating futures, Journal of the Operational Research Society, vol.58, issue.2, p.262270, 2007.

E. Bonabeau, Agent-based modeling : Methods and techniques for simulating human systems, Proceedings of the National Academy of Sciences, vol.99, issue.3, p.72807287, 2002.

B. Heath, R. Hill, and F. Ciarallo, A survey of agent-based modeling practices, Journal of Articial Societies and Social Simulation, vol.12, issue.4, p.9, 1998.

E. Norling, L. Sonenberg, and R. Rönnquist, Enhancing multi-agent based simulation with human-like decision making strategies, in Multi-Agent-Based Simulation, Springer, p.214228, 2000.

E. R. Smith and F. R. Conrey, Agent-based modeling : A new approach for theory building in social psychology, Personality and social psychology review, vol.11, issue.1, p.87104, 2007.

J. M. Epstein, Modelling to contain pandemics, Nature, vol.460, issue.7256, pp.687-687, 2009.

P. A. Johnson and R. Sieber, Agent-based modelling : A dynamic scenario planning approach to tourism pss, Planning support systems : Best practices and new methods, p.211226, 2009.

C. M. Macal and M. J. North, Tutorial on agent-based modeling and simulation, Simulation Conference, p.14, 2005.

T. Murata, Petri nets : properties, analysis and applications, IEEE, vol.77, issue.4, p.541580, 1989.

R. David and H. Alla, Petri nets and grafcet -tools for modelling discrete event systems, 1992.

D. Devapriya, B. Descotes-genon, and P. Ladet, Petri net based node structures for distributed problem solving in fms control, Computer Integrated Manufacturing Systems, vol.5, issue.3, p.229238, 1992.

R. Bouyekhf and A. E. Moudni, On the analysis of some structural properties of Petri nets, IEEE Transactions on Systems, Man and Cybernetics -Part A : Systems and Humans, vol.35, issue.6, p.784794, 2005.

C. Tolba, D. Lefebvre, P. Thomas, and A. Elmoudni, Continuous and timed Petri nets for the macroscopic and microscopic trac ow modelling, Simulation Modelling Practice and Theory, vol.13, issue.5, p.407436, 2005.

L. Amodeo, Contribution à la simplication et à la commande des réseaux de petri stochastiques. application aux systèmes de production, 1999.

H. Abouaissa, B. Rabenasolo, M. Ferney, and P. Vaudrey, Generic models for representing reactive and complex systems using hierarchical coloured petri nets, Petri Net Newsletter, vol.48, p.2748, 1995.

K. Souza and S. Khator, System reconguration to avoid deadlocks in automated manufacturing systems, Computers and Industrial Engineering, vol.32, issue.2, pp.455-465, 1997.

K. Sörensen and J. Janssens, Buer allocation and required availability in a transfer line with unreliable machines, International Journal of Production Economics, vol.74, p.163173, 2001.

K. Sörensen and J. Janssens, A Petri net model of a continuous ow transfer line with unreliable machine, Production, Manufacturing and Logistics, vol.152, p.248262, 2004.

O. Kilincci and G. Bayhan, A Petri net approach for simple assembly line balancing problems, International Journal of Advanced Manufacturing Technology, vol.30, p.11651173, 2006.

K. Labadi, H. Chen, and L. Amodeo, Modeling and performance evaluation of inventory systems using batch deterministic and stochastic Petri nets, IEEE Transactions on Systems, Man, and Cybernetics Part C : Applications and Reviews, vol.36, issue.6, p.12871302, 2007.
URL : https://hal.archives-ouvertes.fr/hal-02498099

M. Minoux, Programmation mathématique, Tec et Doc, 2007.

B. Bunday, An introduction to queueing theory, 1996.

D. D. Almeida, Modélisation par réseaux de les d'attente de systèmes de production, 1996.

D. M. Ferrin, M. J. Miller, and D. L. Mcbroom, Maximizing hospital nanacial impact and emergency department throughput with simulation, Proceedings of the 39th conference on Winter simulation : 40 years! The best is yet to come, p.15661573, 2007.

T. Ruohonen, P. Neittaanmäki, and J. Teittinen, Simulation model for improving the operation of the emergency department of special health care, Proceedings of the 38th conference on Winter simulation. Winter Simulation Conference, p.453458, 2006.

D. Sinreich and Y. N. Marmor, A simple and intuitive simulation tool for analyzing emergency department operations, Proceedings of the 2004 Winter, vol.2, 2004.

A. Wiinamaki and R. Dronzek, Emergency departments i : using simulation in the architectural concept phase of an emergency department design, Proceedings of the 35th conference on Winter simulation : driving innovation. Winter Simulation Conference, p.19121916, 2003.

A. M. Alvarez and M. A. Centeno, Enhancing simulation models for emergency rooms using vba, Simulation Conference Proceedings, vol.2, p.16851693, 1999.

M. J. Miller, D. M. Ferrin, and J. M. Szymanski, Emergency departments ii : simulating six sigma improvement ideas for a hospital emergency department, Proceedings of the 35th conference on Winter simulation : driving innovation. Winter Simulation Conference, p.19261929, 2003.

M. J. Miller, D. M. Ferrin, and M. G. Messer, Fixing the emergency department : A transformational journey with edsim, Proceedings of the 36th conference on Winter simulation. Winter Simulation Conference, 2004.

M. J. Miller, D. M. Ferrin, T. Flynn, M. Ashby, K. P. White et al., Using rd technologies to capture simulation data in a hospital emergency department, Simulation Conference, 2006. WSC 06. Proceedings of the Winter, p.13651371, 2006.

D. C. Lane, C. Monefeldt, and E. Husemann, Client involvement in simulation model building : hints and insights from a case study in a london hospital, Health care management science, vol.6, issue.2, p.105116, 2003.

D. C. Lane, C. Monefeldt, and J. Rosenhead, Looking in the wrong place for healthcare improvements : A system dynamics study of an accident and emergency department, Journal of the operational Research Society, p.518531, 2000.

W. D. Kelton, Simulation with ARENA. McGraw-hill, 2002.

M. L. Delignette-muller and C. , Dutang, and others, tdistrplus : An R package for tting distributions, Journal of Statistical Software, vol.64, issue.4, p.134, 2015.

G. Casella and R. L. Berger, Statistical inference, CA, vol.2, 2002.

A. C. Cullen and H. C. Frey, Probabilistic techniques in exposure assessment : a handbook for dealing with variability and uncertainty in models and inputs, 1999.

M. L. García, M. A. Centeno, C. Rivera, and N. Decario, Reducing time in an emergency room via a fast-track, Simulation Conference Proceedings, p.10481053, 1995.

P. Erard and P. Déguénon, Simulation par événements discrets, PPUR presses polytechniques, pp.2-88074, 1996.

J. Misra, Distributed discrete-event simulation, Computing Surveys, vol.18, issue.1, p.3965, 1986.

I. E. Mahi, O. Grunder, and A. E. Moudni, Modelling-optimization approach for discrete event systems using the (Max,+) algebra and genetic algorithms, International Journal of Innovative Computing, Information and Control, vol.2, issue.4, p.771788, 2006.

A. Yalaoui, Simulation des systèmes industriels, Polycopié du cours SY15, 2009.

L. B. Holm, H. Lurås, and F. A. Dahl, Improving hospital bed utilisation through simulation and optimisation : With application to a 40% increase in patient volume in a Norwegian general hospital, International Journal of Medical Informatics, vol.82, issue.2, p.8089, 2013.

H. Guo and J. Tang, Integrating Simulation with Optimization in Emergency Department Management, in Engineering Asset Management -Systems, Professional Practices and Certication, ser, Lecture Notes in Mechanical Engineering, p.14831496, 2015.

Y. Hani, Optimisation physique et logique d'un établissement industriel : étude de l'eimm de romilly sur seine -sncf, 2006.

D. , Building and testing ecosystems models, Mathematical Models in Ecology, 1972.

J. , Testing ecological models : the meaning of validation, Ecological Modelling, vol.90, issue.3, p.229244, 1996.

W. Kelton, R. Sadowski, and D. Sadowski, Simulation with Arena, 1998.

M. Vamanan, Q. Wang, R. Batta, and R. Szczerba, Integration of COTS software products arena and cplex for an inventory/logistic problem, Computers and Operations Research, vol.31, p.533547, 2004.

T. Yang, Y. Kuo, and P. Chou, Solving a multiresponse simulation problem using a dual-response system and scatter search method, Simulation Modelling Practice and Theory, vol.13, issue.4, p.356369, 2005.

F. Dugardin, H. Chehade, L. Amodeo, F. Yalaoui, and C. Prins, Hybrid Job Shop and parallel machine scheduling problems : minimization of total tardiness criterion. Advanced Robotic System Journal, vol.16, pp.978-981, 2007.
URL : https://hal.archives-ouvertes.fr/hal-02494091

H. Chehade, L. Amodeo, F. Yalaoui, and P. D. Guglielmo, Optimisation multiobjectif appliquée au problème de dimensionnement de buers, Proceedings of LT'07 Logistique et Transport, p.337342, 2007.

N. R. Hoot and D. Aronsky, Systematic Review of Emergency Department Crowding : Causes, Eects, and Solutions, Annals of Emergency Medicine, vol.52, issue.2, p.126136, 2008.

S. Rodier, Une tentative d'unication et de r´esolution des probl`emes de mod´elisation et d'optimisation dans les syst`emes hospitaliers . Application au nouvel h^opital Estaing

J. P. Tupesis, C. Babcock, D. Char, K. Alagappan, B. Hexom et al., Optimizing global health experiences in emergency medicine residency programs : a consensus statement from the Council of Emergency Medicine Residency Directors 2011 Academic Assembly global health specialty track, International Journal of Emergency Medicine, vol.5, issue.1, p.15, 2012.

M. C. Fu and J. Hu, Sensitivity analysis for monte carlo simulation of option pricing, Probability in the Engineering and Informational Sciences, vol.9, issue.3, pp.417-446, 1995.

P. Kim and Y. Ding, Optimal engineering system design guided by data-mining methods, Technometrics, vol.47, issue.3, p.336348, 2005.

M. Semini, H. Fauske, and J. O. Strandhagen, Applications of discrete-event simulation to support manufacturing logistics decision-making : a survey, Simulation Conference, 2006. WSC 06. Proceedings of the Winter, p.19461953, 2006.

S. Andradóttir, Simulation optimization with countably innite feasible regions : Efciency and convergence, ACM Transactions on Modeling and Computer Simulation (TOMACS), vol.16, issue.4, p.357374, 2006.

M. Fu, Optimization for simulation : theory vs. practice, Informs Journal on Computing, vol.14, p.192215, 2002.

L. J. Hong and B. L. Nelson, A brief introduction to optimization via simulation, Simulation Conference (WSC), Proceedings of the, p.7585, 2009.

F. Glover, J. Kelly, and M. Laguna, New advances and applications of combining simulation and optimization, Proceedings of the 28th Winter Simulation conference, p.144152, 1996.

M. H. Alrefaei and S. Andradóttir, A modication of the stochastic ruler method for discrete stochastic optimization, European Journal of Operational Research, vol.133, issue.1, p.160182, 2001.

D. Yan and H. Mukai, Stochastic discrete optimization, SIAM Journal on control and optimization, vol.30, issue.3, p.594612, 1992.

S. Andradóttir, A method for discrete stochastic optimization, Management Science, vol.41, issue.12, p.19461961, 1995.

S. Andradóttir, A global search method for discrete stochastic optimization, SIAM Journal on Optimization, vol.6, issue.2, p.513530, 1996.

S. Andradóttir and A. A. Prudius, Balanced explorative and exploitative search with estimation for simulation optimization, INFORMS Journal on Computing, vol.21, issue.2, p.193208, 2009.

M. H. Alrefaei and S. Andradóttir, A simulated annealing algorithm with constant temperature for discrete stochastic optimization, Management science, vol.45, issue.5, p.748764, 1999.

W. Gong, Y. Ho, and W. Zhai, Stochastic comparison algorithm for discrete optimization with estimation, SIAM Journal on Optimization, vol.10, issue.2, pp.384-404, 2000.

J. Pichitlamken and B. L. Nelson, A combined procedure for optimization via simulation, ACM Transactions on Modeling and Computer Simulation (TOMACS), vol.13, issue.2, p.155179, 2003.

L. Shi, Nested partitions method for stochastic optimization, Methodology and Computing in Applied probability, vol.2, issue.3, p.271291, 2000.

L. J. Hong and B. L. Nelson, Discrete optimization via simulation using compass, Operations Research, vol.54, issue.1, p.115129, 2006.

E. Cabrera, Simulation Optimization for Healthcare Emergency Departments, Procedia CS, vol.9, p.14641473, 2012.

L. Makdessian, Structuration et choix d'équipements des lignes de production : approches mono et multicritère, 2005.

D. Sinreich and Y. Marmor, Emergency department operations : the basis for developing a simulation tool, IIE transactions, vol.37, issue.3, p.233245, 2005.

A. Scholl, Balancing and sequencing of assembly lines, 1999.

S. Kirkpatrick, C. Gelatt, and M. Vecchi, Optimization by simulated annealing, vol.220, p.671680, 1983.

D. E. Goldberg, Genetic algorithms and rule learning in dynamic system control, Proc. of the International Conference on Genetic Algorithms and Their Applications, p.815, 1985.

F. Glover, Tabu search : A tutorial, Interfaces, vol.20, p.7494, 1990.

M. Dorigo, Optimization, learning and natural algorithms, Ph.D. dissertation, Politecnico di Milano, 1992.

B. Descotes-genon, Problème de logistique inverse : utilisation d'une méta-heuristique dans une application de transfert de marchandises, Revue REE, vol.1, p.3440, 2005.

P. Lacomme, C. Prins, and M. Sevaux, Multiobjective capacitated arc routing problem, Lecture Notes in Computer Science, vol.2632, p.550564, 2003.
URL : https://hal.archives-ouvertes.fr/hal-00119861

D. Goldberg, Genetic algorithms in search, optimization and machine learning, 1989.

T. M. Amaral and A. P. Costa, Improving decision-making and management of hospital resources : An application of the PROMETHEE II method in an Emergency Department, Operations Research for Health Care, vol.3, issue.1, p.16, 2014.

L. J. Bara, J. M. Cameron, and R. Sekhon, Direct costs of emergency medical care : a diagnosis-based case-mix classication system, Annals of emergency medicine, vol.20, issue.1, p.17, 1991.

R. R. Roberts, P. W. Frutos, G. G. Ciavarella, L. M. Gussow, E. K. Mensah et al.,

H. E. Kampe, G. Straus, R. J. Joseph, and . Rydman, Distribution of variable vs xed costs of hospital care, Jama, vol.281, issue.7, p.644649, 1999.

S. G. Lynn and A. L. Kellermann, Critical decision making : Managing the emergency department in an overcrowded hospital, Annals of Emergency Medicine, vol.20, issue.3, p.287292, 1991.

T. Chen and C. Wang, Multi-objective simulation optimization for medical capacity allocation in emergency department, Journal of Simulation, vol.10, issue.1, p.5068, 2016.

E. C. Cabrera-flores, Optimization of Healthcare Emergency Departments by Agent-Based Simulation

H. El-maraghy, I. Abdallah, and W. El-maraghy, On-line simulation and control in manufacturing systems, Annals of the CIRP, vol.47, p.401404, 1998.

T. Yang, Y. Kuo, and P. Chou, Optimization of physical ows in an automotive manufacturing plant : some experiments and issues, Engineering Applications of Articial Intelligence, vol.16, p.293305, 2003.

S. Villa, A. Prenestini, and I. Giusepi, A framework to analyze hospital-wide patient ow logistics : Evidence from an Italian comparative study, Health Policy, vol.115, issue.23, 2014.

F. Mallor and C. Azcárate, Combining optimization with simulation to obtain credible models for intensive care units, Annals of Operations Research, vol.221, issue.1, p.255271, 2011.

T. Yu, D. E. Goldberg, A. Yassine, and Y. Chen, Genetic Algorithm Design Inspired by Organizational Theory : Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm, p.16201621, 2003.

I. H. Osman and J. P. Kelly, Meta-heuristics : an overview, Meta-heuristics. Springer, p.121, 1996.

M. Pillet, Les plans d'expériences par la méthode Taguchi, 2001.

L. Davis, Handbook of genetic algorithms, 1991.