Stateless Two-Stage Multiple Criteria Scheduling in Nuclear Medicine

Examination in nuclear medicine exhibits scheduling difficulties due to its intricate clinical issues, such as varied radiopharmaceuticals for different diseases, machine preparation and length of scan, and patients’ and hospital’s criteria and/or limitations. Many scheduling methods exist but are limited for nuclear medicine. In this paper, we present stateless two-stage scheduling to cope with multiple criteria decision making. The first stage mostly deals with patients’ conditions. The second stage concerns more the clinical condition and its correlations with patients’ preference which presents more complicated intertwined configurations. A greedy algorithm is proposed in the second stage to determine the (time slot and patient) pair in linear time. The result shows practical and efficient scheduling for nuclear medicine

Novel approaches to radiotherapy treatment scheduling

Radiotherapy represents an important phase of treatment for a large number of cancer patients. It is essential that resources used to deliver this treatment are used efficiently. This thesis approaches the problem of scheduling treatments in a radiotherapy centre. Data about the daily intake of patients are collected and analysed. Several approaches are presented to create a schedule every day. The first presented are constructive approaches, developed due to their simplicity and low computational requirements. The approaches vary the preferred treatment start, machine utilisation reservation levels, and the frequency and number of days in advance with which schedules are created. An Integer Linear Programming (ILP) model is also presented for the problem and used in combination with approaches similar to the ones above. A generalisation of the constructive utilisation threshold approach is developed in order to vary the threshold level for each day according to how far it is from the current day. In addition, the model is evaluated for different sizes of the problem by increasing the rate of patient arrivals per day and the number of machines available. Different machine allocation policies are also evaluated. An exact method is introduced for finding a set of solutions representing the whole Pareto frontier for integer programming problems. It is combined with two robust approaches: the first considers known patients before they are ready to be scheduled, while the second considers sets of predicted patients who might arrive in the near future. A rescheduling approach is also suggested and implemented. A comparison is made amongst the best results from each group of approaches to identify the advantages and disadvantages of each. The robust approaches are found to be the best alternative of the set

Adaptive Optimization of Hospital Resource Calendars

As demand for health care increases, a high efficiency on limited resources is necessary for affordable high patient service levels. Here, we present an adaptive approach to efficient resource usage by automatic optimization of resource calendars. We describe a precise model based on a case study at the radiology department of the Academic Medical Center Amsterdam (AMC). We model the properties of the different groups of patients, with additional differentiating urgency levels. Based on this model, we develop a detailed simulation that is able to replicate the known scheduling problems. In particular, the simulation shows that due to fluctuations in demand, the allocations in the resource calendar must be flexible in order to make efficient use of the resources. We develop adaptive algorithms to automate iterative adjustments to the resource calendar. To test the effectiveness of our approach, we evaluate the algorithms using the simulation. Our adaptive optimization approach is able to maintain overall target performance levels while the resource is used at high efficiency

Adaptive optimization of hospital resource calendars

As demand for health care increases, a high efficiency on limited resources is necessary for affordable high patient service levels. Here, we present an adaptive approach to efficient resource usage by automatic optimization of resource calendars. We describe a precise model based on a case study at the radiology department of the Academic Medical Center Amsterdam (AMC). We model the properties of the different groups of patients, with additional differentiating urgency levels. Based on this model, we develop a detailed simulation that is able to replicate the known scheduling problems. In particular, the simulation shows that due to fluctuations in demand, the allocations in the resource calendar must be flexible in order to make efficient use of the resources. We develop adaptive algorithms to automate iterative adjustments to the resource calendar. To test the effectiveness of our approach, we evaluate the algorithms using the simulation. Our adaptive optimization approach is able to maintain overall target performance levels while the resource is used at high efficiency