Because the local search procedure takes a very long time, two local search procedures were implemented.

- One with a smaller neighborhood, which doesn't guarantee us to derive the global optimum, but gives good results within a reasonable time.

- One with a big neighborhood, which guarantees us to derive the global optimum, but the computing time can be very long.

The method of this tool is described in a HCMS publication.

Our goal is to schedule these patients over those intervals in such a way that it minimizes the weighted average of expected waiting times of patients, idle time of the doctor and tardiness.

In the model we make the following assumptions:

- The service times of patients are independent and exponential distributed.

- Patients come always right on time.

- Every patient has the same chance of not coming at all (no-show).

- n: the nth patient.

- N: the total number of patients.

- I: the total number of intervals.

- T: the length of an interval.

The number of intervals are numbered from 1, ..., I.

Patient n arrives in interval round((n-1) * I / N)+1 at time round((n-1) * I / N) * T.

For example: N = 5 patients, I = 8 intervals and T = 15 minutes.

Patient 3 arrives in interval round((3-1)*8/5)+1 = 4 at time round((3-1)*8/5)*15 = 00:45.

- Total number of arrivals: | The total number of patients which must be treated | |

- Average service time: | The average time for a treatment | |

- Total available time: | The total available time of the system | |

- Schedule in intervals of ... minutes: | The length of an interval in minutes | |

- Percentage no-shows: | Which percentage of patients are not coming at all? | |

- Patient-centric: | There isn't a best way to choose weights. It depends on the objective. So if the main objective is patient-centric, then that factor must have a bigger weight. | |

- Doctor-centric: |

- Waiting time: | This is the average time a patient is waiting for her/his treatment. | |

- Idle time: | This is the average total time within the span a doctor is waiting for a patient. | |

- Tardiness: | This is the probability that the session exceeds the planned finishing time times the average excess. | |

- Fraction of excess: | This is the probability that the last patient finishes its treatment after the end of the working day/block. | |

- Make span: | This is the average time in which all patients have been treated. | |

- Lateness: | Day/block length -/- make span |

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