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PMC6452836_02
Results static and dynamic scheduling with  for multiple cancellation probabilities.
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Figure 1 depicts simulation results for different cancellation probabilities, performance measures and scheduling policies. The simulated scenario with 15% cancellation probability has insufficient capacity in the MIP scenario to schedule all arriving patients and is therefore not depicted in Figure 1. With the current capacity and 10% cancellations 38.3% of the patients cannot be scheduled within one week, see Figure 1(c). Figure 1(d) depicts how often the MDP policy prescribes to schedule an additional block; this policy implies that for the 10%-scenario on average 2.5% capacity is added to the schedule. From Figure 1(a) and (c) it appears that the MDP policy outperforms the MIP+ policy with respect to both the access and idle time, i.e., dynamic scheduling outperforms static scheduling already for this small fraction of flexible capacity. Similar to the MIP, Figure 1 only depicts the results of the MDP with equal costs for access and idle time.
Week-dependent results for 10% cancellation.
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Figure 2 we depict simulation results of the conditional average access time of patients arriving in week x for several scheduling policies with 10% cancellation probability. From these results it is clear that pooling the capacity can already improve the performance significantly.
Week-dependent results for 5% cancellation.
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We depict simulation results for  and  in Figures 3 and 4, respectively, and in Table 3.
Week-dependent results for 15% cancellation.
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We depict simulation results for  and  in Figures 3 and 4, respectively, and in Table 3.
Results static and dynamic scheduling for fluctuating arrival rates for 10% cancellation.
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The results for these simulations are depicteds in Figures 5 and 6. As a consequence, patient access times increase, as Figures 5 and 6 depict, especially for the MIP schedule.
Results static and dynamic scheduling for strongly fluctuating arrival rates for 10% cancellation.
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The results for these simulations are depicteds in Figures 5 and 6. As a consequence, patient access times increase, as Figures 5 and 6 depict, especially for the MIP schedule.
Flexible schedule simulation results of different weekly capacity distributions and .
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