The emergency department (ED) is a primary health care unit and one of the main entrances to the hospital system where appropriate, timely and good performance can save lives. Lack of sufficient resources, such as beds and qualified health care professionals, are major stumbling blocks to providing timely and suitable services; but resources availability and moving towards the ideal situation without attention to budget restrictions is neither practical nor achievable. In this study, simulation optimization is used to finding the best configuration in ED resources (e.g., Bed, Nurse, and GP) that affects a patient's length of stay, subject to budget constraints. Simulation is used to analyze the system and estimate target function an optimization model is then solved under different budget constraints. By considering the current budget, the new configuration of 20 inpatient beds, 3 nurses and 1 GP, with 554.4 minutes of a patient's length of stay shows 8.1% length of stay (LOS) improvement. Whilst with a maximum 35.5 budget units allocation of 20 inpatient beds, 4 nurses and 3 GPs a 9.5% decrease in LOS is proposed.  A healthcare system is one of the most important services with rapid growth demands in both developed and less developed countries. The main focus of a health care system is on the patients who have different expectations, in different times and situations [Faezipour and Ferreira, 2013] . Indeed, in recent years changing patterns of disease, progress in medical knowledge and technology, public awareness of modern medical facilities, along with an increasing elderly population have resulted in greater demand for medical services, as well as more pressure on the health care budget. In this way, attempts to prevent the rising costs of healthcare by better distribution of scarce resources and by applying cost-containment strategies are major challenges for healthcare authorities [Ferreira et al., 2011] . The emergency department (ED) as a primary health care unit and one of the main entrances to the hospital system, therefore necessitates considerable attention. Proper, correct and timely function in an ED can save many lives and decrease various adverse effects. This requires an understanding and the scrutiny of the current situation in this sector. In many cases, lack of resources such as beds, qualified health care professionals and nurses in care prevent timely and suitable services. This can also lessen health care quality, increase potential medical errors, add to long waiting times and increase a patient's length of stay (LOS), which is followed by deleterious effects on the patient, as well as unintended higher costs for service providers. Hence, an equilibrium between supply and demand can provide obvious improvement in service quality and bring higher patient satisfaction in the emergency department [Xu et al., 2013] . Optimum resource allocation in the healthcare sector and improving the quality of communal health is important and has always been considered as a controversial issue [Asante and Zwi, 2009] . Because of scarce and costly resources, as well as sensitive, yet challenging conditions, a large body of literature has noted the allocation of health care resources. Laker et al, [2014], by using discrete-event simulation, demonstrated that adding some flexibility into bed allocation between low-and high-acuity can provide substantial reductions in overall patient waiting and efficiency in ED. Holm et al, [2013] analyzed bed allocation in hospital wards to minimize overcrowding by developing a novel, generic discrete event simulation (DES) model. The number of beds in a hospital and the impact on hospital operations, length of stay, as well as waiting lists were studied by Reyes-Santías et al, [2013]. Schmidt et al, [2013] focused on improving bed assignment by considering adaptable length of stay estimation, which results in a 30% reduction of the dismissal rate. Bekker and Koeleman [2011]addressed the impact of resource variability in determining capacity requirements and admission quota for planned admissions to regulate the pattern of bed occupancy. Griffiths et al, [2010], determined the number of beds that were needed in the critical care unit (CCU) with discrete event simulation. Optimal allocation of human resources is one appropriate strategy to meet demand and provide suitable service with high quality, to increase patient and personal satisfaction. Lim et al, [2013], modelled physicians and their delegates in the ED as interacting pseudo-agents in a discrete event simulation and compare it with the traditional approaches. They found with this approach physician utilization increase from 23% to 41% and delegate utilization increase from 56% to 71%. Izady and Worthington [2012] demonstrated how queuing models with simulation can be used to reduce overcrowding in ED by modifying the staffing profiles. Brenner et al, [2010] identified bottlenecks and evaluation of human resources and equipment in ED using simulation. In the ED, with several dynamic variables and random features, such as unscheduled admissions, irregular patient arrival times as well as multiple types of patients who require various care processes, resource allocation seems a more important issue than in other wards. Shimada et al, [2012], determined the resource utilization of a tertiary care, Japanese emergency department before and after the 2011 Great East Japan earthquake and tsunami to help planning the allocation of national resources for disasters. Ahmad et al, [2012] in their paper presented a computer simulation model to assess the use of resource utilization in the ED of a public hospital in Malaysia. By using this model administrators could monitor the patient flow in ED, determine possible areas for improvement and ensure the best resource allocation. Weng et al, [2011], stated that the purpose of their research was to find an optimal allocation of resources in the ED through simulation. They used Simul8 and the results showed overall performance in an ED can increase by 8% through allocating new human resources. Cochran and Broyles et al, [2010], proposed making strategic decisions for future ED capacity, based on patient safety (rather than congestion measures) in their study. Ng et al, [2010], compared priorities and the differences between resource utilization in four-levels of Taiwan triage and five-levels of Canadian
In the research of REF , researchers used a simulation optimization approach to find the appropriated configuration in 'ED' resources, including nurses, general practitioners, and beds.
A simulation optimization approach for resource allocation in an emergency department
QScience Connect
Medicine