Sağlık kurumları için riske duyarlı randevu planlaması

In line with the consistent rise in health expenses in the last decades, operations research based decision-making has gained an important place in the healthcare management. Appointment scheduling is a widely addressed research area for the healthcare management as its performance is very influential in enhancing on-site services offered and reduce operational costs. In this thesis, we develop two different two-stage risk-averse stochastic programming models to solve the appointment scheduling problem for diagnostic/treatment clinics while considering different sources of uncertainty. The no-show and waiting times of the patients and overtime working conditions of the doctors are included in the formulation of both models. The first model is total cost reduction oriented while the scope of the second model is raising revenue with the addition of walk-in patients. We characterize the random parameters by finite sets of scenarios and use conditional value-at-risk measure to control the possible large realizations of random outcomes. We obtain the optimum appointment times by the variants of the L-Shaped algorithm developed. We also conduct a computational study to illustrate the effectiveness of the proposed modelling approaches.