Nurses Staffing and Allocation in Multi-stage Queueing Network with I2 Patients Routing for Outpatient
Department
Abstract:
A general multi-stage queuing network model with I2 patients
routing including two tandem queues is established to formulate the behavior
of patients flow in Outpatient Department (OD) in a hospital starting from registration,
diagnosis, chemical examination, referral, payment and medicine-taking. Focusing
on the nurse resources, the formula of performance indicators such as patient
waiting time, probability of nurse
idle are derived by using the system parameters. A mathematical programming
model is developed to determine how many nurses are needed and how to allocate
to each stage/division to minimize the total costs of patients
waiting time and the nurses
idle time. How to allocate the nurse to each stage is essentially a natural
number decomposition problem and thus a neighborhood search combined Simulated
Annealing (NS-SA) with Heuristic is developed. Optimal nurse numbers are derived
from the enumeration method based on NS-SA. Numerical experiments are conducted
to analyze the impact of patients
arrival to the allocation of nurses and the ratio of patients
waiting time and nurses idle
time on the number of nurses needed. The research results can facilitate hospital
managers to make decisions on human resources in practice.
How to cite this article
Huabo Zhu, Jiafu Tang and Jun Gong , 2013. Nurses Staffing and Allocation in Multi-stage Queueing Network with I2 Patients Routing for Outpatient
Department. Journal of Applied Sciences, 13: 2884-2890.
REFERENCES
Abadi, I.N.K., G.N. Hall and C. Sriskandarajah, 2000. Minimizing cycle time in a blocking flowshop. Operat. Res., 48: 177-180.
Direct Link
Au-Yeung, S.W.M., P.G. Harrison and W.J. Knottenbelt, 2006. A queueing network model of patient flow in an accident and emergency department. Model. Simulati., 4: 60-67.
Direct Link
Filipowicz, B. and J. Kwiecien, 2008. Queueing systems and networks. Models and applications. Bull. Polish Acad. Sci., 56: 379-390.
Direct Link
Balsamo, S., V. de Nitto Persone and R. Onvural, 2001. Analysis of Queueing Networks with Blocking. Kluwer Academic Publishers, Boston
Creemers, S. and M. Lambrecht, 2011. Modeling a hospital queueing network. Int. Ser. Operat. Res. Manage. Sci., 154: 767-798.
Direct Link
Osorio, C. and M. Bierlaire, 2009. An analytic finite capacity queueing network model capturing the propagation of congestion and blocking. Eur. J. Operat. Res., 196: 996-1007.
Direct Link
El-Darzi, E., C. Vasilakis, T. Chaussalet and P.H. Millard, 1998. A simulation modelling approach to evaluating length of stay, occupancy, emptiness and bed blocking in a hospital geriatric department. Health. Manag. Sci., 1: 143-149.
PubMed Direct Link
Hall, R., D. Belson, P Murali and M. Dessouky, 2006. Modeling Patient Flows Through the Healthcare System. In: Patient Flow: Reducing Delay in Healthcare Delivery, Hall, R. (Ed.)., Springer, New York
Helm, J.E., S. AhmadBeygi and M.P. van Oyen, 2011. Design and analysis of hospital admission control for operational effectiveness. Prod. Oper. Manag., 20: 359-374.
CrossRef
Zhu, H., J. Gong, and J. Tang, 2013. A queuing network analysis model in emergency departments. Proceedings of the 25th Chinese Control and Decision Conference, May 25-27, 2013, Guiyang, China -.
Jlassi, J., El Mhamedi, A. and H. Chabchoub, 2010. Networks of queues with multiple customer types: application in emergency department. Int. J. Behavioural Health. Res., 1: 400-419.
Direct Link
Jackson, J.R., 1963. Jobshop-like queueing systems. Manage. Sci., 10: 131-142.
CrossRef
Jackson, J.R., 1957. Networks of waiting lines. Operations Res., 5: 518-521.
CrossRef
Kirkpatrick, S., C.D. Gelatt Jr. and M.P. Vecchi, 1983. Optimization by simulated annealing. Science, 220: 671-680.
CrossRef Direct Link
Koizumi, N., E. Kuno and T.E. Smith, 2005. Modeling patient flows using a queuing network with blocking. Health. Care Manag. Sci., 8: 49-60.
PubMed Direct Link
Bretthauer, K.M., H.S. Heese, H. Pun and E. Coe, 2011. Blocking in healthcare operations: A new heuristic and an application. Prod. Operat. Manage., 20: 375-391.
CrossRef
Robinson, L.W. and R.R. Chen, 2010. A comparison of traditional and open-access policies for appointment scheduling. Manuf. Ser. Operat., 12: 330-346.
CrossRef
Ahmed, M.A. and T.M. Alkhamis, 2009. Simulation optimization for an emergency department healthcare unit in Kuwait. Eur. J. Operat. Res., 198: 936-942.
Direct Link
Izady, N. and D. Worthington, 2012. Setting staffing requirements for time-dependent queueing networks: the case of accident and emergency departments. Eur. J. Operat. Res., 219: 531-540.
Direct Link
Price, C., B. Golden, M. Harrington, R. Konewko, E. Wasil, W. Herring, 2011. Reducing boarding in a post-anesthesia care unit. Prod. Operati. Manage., 20: 431-441.
CrossRef
Ruger, J.P., L.M. Lewis and C.J. Richter, 2007. Identifying high-risk patients for triage and resource allocation in the ED. Am. J. Emerg. Med., 25: 794-798.
PubMed
Tavares, R.S., T.C. Martins and M.S.G. Tsuzuki, 2011. Simulated annealing with adaptive neighborhood: A case study in off-line robot path planning Expert Syst. Appl., 38: 2951-2965.
CrossRef
Vasan, A. and K.S. Raju, 2009. Comparative analysis of simulated annealing, simulated quenching and genetic algorithms for optimal reservoir operation. Applied Soft Comput., 9: 274-281.
CrossRef
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