A Two-Stage Method of Dimensioning and Scheduling Service Providers under Patient Demand Uncertainty

<table class="algorithm-group"><tr><td><table class="algorithm" id="alg2"><tr><td>(1)</td><td>X : set of data points</td></tr><tr><td>(2)</td><td>k: number of clusters</td></tr><tr><td>(3)</td><td>Randomly initialize 1 cluster centers(centroids) from X</td></tr><tr><td>(4)</td><td>Select k-1 cluster centers(centroids) systematically with a probability that is proportional to their contribution to the overall error from X</td></tr><tr><td>(5)</td><td>Repeat</td></tr><tr><td>(6)</td><td> Expectation: assign each point to its closest centroid.</td></tr><tr><td>(7)</td><td> Maximization: the mean of all points belonging to each cluster specifies the new centroid</td></tr><tr><td> </td><td>Until the centroid positions converge to a constant value</td></tr></table></td></tr></table>

Computational Intelligence and Neuroscience

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Algorithm 2

Algorithm 2: A Two-Stage Method of Dimensioning and Scheduling Service Providers under Patient Demand Uncertainty 