Table 6 Comparing LRCVaR models with different clustering methods (\(\alpha\)=0.1).
From: Data-driven robust outpatient physician scheduling with medical visiting information
Model | Shifts | Service capacity shortage | Service capacity excess | ||||
|---|---|---|---|---|---|---|---|
Count | Mean | Max | Prob (%) | Mean | Max | Prob (%) | |
Service capacity \(c_i = 60\) | |||||||
LRCVaR+GMM | 788 | 1.2 | 61.1 | 4.2 | 60.0 | 328.4 | 95.8 |
LRCVaR+K-means | 768 | 1.3 | 116.5 | 6.5 | 59.5 | 286.1 | 94.3 |
Service capacity \(c_i = 70\) | |||||||
LRCVaR+GMM | 660 | 0.6 | 42.8 | 3.4 | 57.2 | 333.4 | 96.6 |
LRCVaR+K-means | 660 | 1.0 | 149.8 | 4.8 | 58.4 | 290.8 | 95.2 |
Service capacity \(c_i = 80\) | |||||||
LRCVaR+GMM | 584 | 0.9 | 31.1 | 5.0 | 58.5 | 303.1 | 95.0 |
LRCVaR+K-means | 576 | 1.4 | 152.7 | 5.3 | 58.5 | 337.3 | 94.7 |
