# A Review of the Scheduling Problem within Canadian Healthcare Centres
## 4 Hospital Scheduling in Depth
### Operating Room Scheduling
#### 4.3.2 Papers
After a pool of initial solutions is generated, it is then solved using a multi-agent tabu search that attempts to optimize waiting and completion time. With two variables to consider, there is an infinite number of \"optimal\" solutions that lay on a Pareto front. The model iterates through the tabu search, trying to converge on the Pareto front. Their model was compared against NSGA-II, a popular genetic algorithm on the metrics of closeness to the Pareto frontier and smoothness of distribution. The new model performed better on both cases and additionally had less computation time. In addition to the model creation, a case study was done in a large Canadian hospital which houses approximately 500 beds, 10 ORs, and performs 8000 surgeries every year. Their methodology was able to offer solutions that were equal to, if not better than, the ones used previously. It also allowed more flexibility, as aspects such as overtime, cost, completion time, and more could be optimized for. This procedure offered some insight into how schedules should be created in the future. Overtime should be minimized at all costs, as it leads to fewer mistakes and decreases financial cost. Longer appointments should be scheduled early or in the middle of the day as they are the most variable, and scheduling them later in the day can lead to overtime. Surgeons should be in the same room as this will limit turnaround time. The least flexible appointments should be scheduled first, so if flexibility is needed the more flexible appointments can bear it. Finally, similar cases should be scheduled in the same OR to maximize throughput. [PERSON] et al. [35] look at a very similar problem, but with a different methodology. They also attempt to solve the problem of operating room appointment scheduling while optimizing wait time and completion time, and consider the number of cancellations. This time they propose three models, all of which are simulation-based tabu searches. The first is technique is simulation tabu search (SBS). This starts with an arbitrary solution not generated by a mathematical programming model. It is then further iterated upon by using both an integer programming model and a binary programming model to generate the initial candidate solutions. The simulation model is a discrete event model that outputs the stochastic elements such as how many patients arrive and of what type. The simulation runs for 30 replications and returns the results. The initial model, which used an arbitrary starting solution, performed worse than the other two and was mainly used as a stepping stone to define the others. Both the integer and binary versions produced similar solutions, but the integer version was much slower. An integer solution is much more restrictive than a binary one, so the Binary Programming Enhanced tabu search (BPETS) was recommended for practicality. After creating the model, a case study was run using BPETS at an important hospital in Canada. This led to a large reduction in completion times in their OR department. [PERSON] et al. [37] offer a final look into building an operating room scheduler in the paper \"A discrete event simulation evaluation of distributed operating room scheduling\". This paper utilizes a deterministic integer model called DORS for scheduling patients. The model is then evaluated using a discrete event simulation (DES) modelling. The DES model was built using data from the University Health Network which includes Toronto General Hospital (TGH), Toronto Western Hospital (TWH), and Princess Margaret Cancer Center (PMCC). The model contains three main parts: the patient arrivals, the ORs, and the downstream units. Patients arrive and are waitlisted each day. At midnight they are scheduled one by one by priority, or until OR capacity is reached. Like previous papers, there are two main categories of patients to arrive: elective or expected patients, and emergency patients, which are random.