Table 11 The comparison between the models.
Authors | Model | Solution | Optimal function | Results |
|---|---|---|---|---|
Rashid et al. (2020) | Hybridized WOAGWO with Solution Approach | Solving critical probability in pressure vessel design in hospital | Statistical test compute for unimodal and multimodal functions | Shows that WOAGWO outperforms other algorithms depending on the test |
Oliva et al. (2020) | Mixed integer linear programming (MILP), two binary variants of WOA | Minimized cost feature and total waiting time | A particle swarm optimizer and a mixed statistical test called | Achieved the smallest number of selected features with the best classification accuracy in a minimum time |
Tahir et al. (2020) | Implementing two stages, binary chaotic (BCGA) and WOA | Two stages fitness function and BCGA feature given significant classification accuracy | Evolutionary computing-based optimized patient’s waiting time | BCGA map perform better and find a robust subset as compared to other maps in enhancing the performance of raw WOA |
The presented method | Inter Linear Programming and WOA technique | Resolves appointment scheduling and waiting time problems for effective FCFS policy and obtained patients satisfaction | Numerical results indicate that both the FCFS and WOA approaches are strategy optimized | Both the FCFS and the WOA strategies data to gain the most propriety (Fairness) results and patient satisfaction |
