# A Review of the Scheduling Problem within Canadian Healthcare Centres
## 8 Future Work  
As one can see, there has been a very diverse set of solutions being used to tackle the problems above, most of which were able to improve upon current methods and more accurately allocate resources. Many different ideas have been explored on how to efficiently optimize the solutions generated by a programming model, most frequently neighbourhood searches and tabu search. Other metaheuristic algorithms such as particle swarm optimization, genetic algorithms, and more have only little to no representation here, while research has been done on these algorithms, none has been done in this domain. Future researchers could investigate more efficient ways to improve upon solutions. The creation of the actual mathematical model is a fairly fleshed-out process across these papers, so less effort would need to be directed towards that. Additional ways to reduce the problem, however, is another important area. Decomposition of problems into subproblems through processes such as Bender's Decomposition or Column Generation allow more flexible, generic, and complex models by allowing easier computation. By exploring these topics more advanced models could be generated and their effects explored. Another area of interest that is more specific to this domain surrounds the research that focuses on making predictions of stochastic elements. Being able to know the approximate number of patients, when they will arrive, how long their duration will be, etc. can improve models beyond simply improving a solution. This helps define the boundary of where the solution should be improved in the first place. There were a few papers that looked into these problems, but there are still many more ideas that have yet to be explored. One area of further research could be to use machine learning to attempt to model these elements. Another would be to compare different simulation techniques to identify which performs the best in Canadian healthcare environments. The more accurately the models can gauge these variables, the more accurate the final solutions will be. Exploring how to encode data is an area that most papers have mostly neglected. A large majority of papers encode their schedules by creating a sequential list of timeslots. As [PERSON] et al. [21] has demonstrated, there are other paradigms to consider that can lead to faster and more effective models. [PERSON] et al. [45] and [PERSON], [PERSON], and [PERSON] [13] also touch on this idea by suggesting a horizontal scheduling method (per week or schedule) and comparing it to a vertical (per day) method. Each version of the data has different benefits. Computation time could be improved by changing the method of encoding the data. Collaborative work also looked very promising as an area that warrants more explanation. When treating hospitals as a network more resources are available for allocation. Scheduling becomes a collaborative process, and so lesser utilized areas can redirect resources to the location that needs them most. [PERSON] et al. [2] are the only people who explore this area, and they have positive results to back it up. Canadian hospitals that work together should be able to lessen the burden each one experiences individually. Additionally, there is very little representation of hospitals that do not originate from Montreal, Toronto, and Ottawa. Smaller hospitals tend to have different problems compared to larger, more urban ones. Branching out and exploring hospitals that represent different demographics could offer valuable insights that are not available in large cities. Many papers also fail to follow up with how their scheduler impacts the hospital. There is no data to validate how these schedulers affect metrics such as well being or patient outcome over longer periods of time. This data would be beneficial in order to explore which constraints are valued by staff and quantify the impacts each choice has. Adoption is limited once the research period has ended, work could be done on how to encourage use beyond the trial period.