Discrete simulation application-scheduling staff for the emergency room
WSC '89 Proceedings of the 21st conference on Winter simulation
WSC '92 Proceedings of the 24th conference on Winter simulation
The physics of the Mt/G/ ∞ symbol Queue
Operations Research
Reducing time in an emergency room via a fast-track
WSC '95 Proceedings of the 27th conference on Winter simulation
A simulation model for evaluating personnel schedules in a hospital emergency department
WSC '96 Proceedings of the 28th conference on Winter simulation
A tutorial on simulation in health care: applications issues
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Emergency department simulation and determination of optimal attending physician staffing schedules
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 2
Proceedings of the 35th conference on Winter simulation: driving innovation
Emergency departments II: a simulation-ilp based tool for scheduling ER staff
Proceedings of the 35th conference on Winter simulation: driving innovation
Modeling emergency departments using discrete event simulation techniques
WSC '05 Proceedings of the 37th conference on Winter simulation
Expert Systems with Applications: An International Journal
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Our research was motivated by the resource allocations problem in the Emergency Department at the Prince of Wales Hospital in Hong Kong. We adopted a simulation approach to analysis how the allocation decisions impact patient's experience in the department. The development of the model is complicated by the fact that there are different categories of patients (with different time-varying arrival rates, treatments and procedures), and the data records were incomplete to allow direct estimation of many of the key operational parameters (e.g. the duration of doctor's consultation). To tackle the first issue, patients' arrivals are modelled as Poisson processes with category and time-dependent arrival rates. The second issue is resolved by positing a general distribution (Weibull) for some key processes, and developing meta-heuristic approaches to jointly estimate the distribution parameters. Our computational results show that accurate estimates of the distribution parameters are found using our proposed search procedure, in that the simulated results and the actual data were consistent.