Modelling and simulation of workforce assignment for the hospital pharmacy - Novitria Fajar Ilmayani (2017)
This report outlines the documentation of development of model for a highly automated pharmacy in the Changi General Hospital. In such hospital, an automatic dispensing machine dispenses prescribed drugs. The drugs are then picked as a batch, with the maximum batch size being six drugs. The drugs are then picked to be delivered in bulk. Research proves that benefits of automation is context specific and is affected largely by number of servers employed in the system. The hospital management thus seeks to reap the benefit of automation to free pharmacists of tedious and labor-consuming tasks while decreasing patient waiting time. Since staffing is a time-consuming process that has a lasting effect, simulation is explored as a means to determine the appropriate number of staff to employ. The process of arrival of prescribed drugs were found to be appropriately modelled by a stationary Poisson process. It is then picked and delivered in a service time that was found to have negligible variance and is therefore modelled as discrete. Using MATLAB SimEvents, a discrete simulation software, the queue is modelled with provided blocks and user-defined blocks to ensure the application of appropriate logic flow. The simulation is then run with various parameters with a warmup of 200,000 entities for steady state and then 30 samples of 100,000 entities to ensure appropriate size for sampling. The waiting time of entities, defined as the length of time entities spend in the FIFO queue is then compiled to give an average waiting time. Simulation result shows appropriate staffing and recommendation for changes in the parameter of the setting. Further refinement of the model comes with more complex data of server failure and extreme case of variance when drugs dispensed is found to be incorrect. Modification of model for other cases by including time-out rate of entities. Further research can also be done to model such case using nonstationary Poisson rate where appropriate.