Huabo Zhu
College of Information Science and Engineering, Northeastern University, State Key Laboratory of Synthetical Automation for Process Industries (Northeastern University), Shenyang, Liaoning, 110819, China
Jiafu Tang
College of Information Science and Engineering, Northeastern University, State Key Laboratory of Synthetical Automation for Process Industries (Northeastern University), Shenyang, Liaoning, 110819, China
Jun Gong
College of Information Science and Engineering, Northeastern University, State Key Laboratory of Synthetical Automation for Process Industries (Northeastern University), Shenyang, Liaoning, 110819, China
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.
PDF References Citation
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.
DOI: 10.3923/jas.2013.2884.2890
URL: https://scialert.net/abstract/?doi=jas.2013.2884.2890
DOI: 10.3923/jas.2013.2884.2890
URL: https://scialert.net/abstract/?doi=jas.2013.2884.2890
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 - 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.
PubMedDirect Link - 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 - 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 - Kirkpatrick, S., C.D. Gelatt Jr. and M.P. Vecchi, 1983. Optimization by simulated annealing. Science, 220: 671-680.
CrossRefDirect 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.
PubMedDirect 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