Recommendations for Future Work

There are several opportunities to improve upon the two phase approach developed in this paper. The first recommendation is also a limitation on the current version of the model. The simulation-based optimization model currently requires several days of run time to produce solutions. The model often tests scenarios that are extremely costly and would not be feasible for the OR. This is because there is a lack of constraints on the model. An improvement that would greatly increase the usability of the Nurse Planning phase would be to generate better and stricter constraints on the model so that the alternative solutions tested are all part of the feasible set of solutions for the OR.

Another possible adjustment would be to increase the size of the OR system being tested. That is, the models should be tested using more operating rooms, more surgical specialties, and a larger set of shifts to choose from. There are many hospitals that have more that have more than the 6 or 12 ORs suggested in this model and it is unclear how the increase in number of ORs will increase the run time of the simulation model as well as the assignment model. Also, with a larger set of shifts, the simulation will have an even greater set of scenarios to choose from

which increase the overall run time, but also may provide a solution that could reduce cost even further than a simulation with fewer shifts.

Further testing on different weights of the assignment model can be done. For the weight on the satisfaction portion of the objective, βi, the current model assumes each nurse to have the

same weight. However, if a hospital wanted to account for seniority of nurses, a higher weight could be given to nurses who have worked at the hospital the longest. This would ensure that they receive better schedules overall. Another weight that could be tested further is the θ1 and θ2

parameters. Each portion of the assignment model objective can be weighted more heavily by adjusting these parameters. A scheduler could compare the results of the model when using multiple combinations of θ values to find the best model to represent the hospital. As the needs of the hospital change, these parameters can be adjusted again to accommodate.

A final recommendation is for the assignment model. Currently the model assumes that each nurse has the same rating of each of the available shifts. However it is conceivable that different nurses would have different ratings for each shift based on the nurse’s preferences. The γj parameter should be expanded to include a value for each nurse, γij. This will give even more

flexibility to the model in that it will be able to account for each individual nurse’s preference for each shift. This could add a lot of value to the solution depending on the needs of the OR, but may also increase the time required to obtain a solution.

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