Tesi di LAUREA SPECIALISTICA Titolo Un approccio robusto per il problema dell'assegnamento ottimale dei pazienti agli operatori nei servizi di assistenza domiciliare Data 2015-05-02 Autore/i Matteo CINISELLI Relatore Carello, G. Relatore Lanzarone, E. Full text non disponibile Abstract An excellent management of Health's resources is important, because it allows to reduce costs that managing authority has to support and to improve quality of supplied services. Home Care includes medical, paramedical and social services which are delivered to patients directly at their own domicile. Management of human and material resources in Home Care services is a complex task, as the provider has to deal with peculiar constraints, for example continuity of care, which imposes than a patient is always cared for by the same nurse. This treatment allows to reduce the possibility of losing informations concerning patient's condition and ongoing treatment, but it reduces drastically the flexibility of nurses' turns. High variability of patients' demand makes the problem more complex. One of the main issues in Home Care services' management in the case of continuity of care is the choise of nurse-to-patient assignment. We provide a model that is able to find nurse-to-patient assignment minimizing supported costs and maximizing quality of provided service. Uncertainty of patients' demand is another relevant feature of nurse-to-patient assignment problem and it is usually managed adopting stochastic programming or analytical policies. However, both these approaches have limits, as they require high computational time and generate sometimes too conservative solutions. In this thesis we develop two model of robust assignment, which allows to exploit the potentialities of a mathematical programming, focusing on a detailed description of the evolution of patients' demands. The models are tested on real-life instances in order to assess the quality of proposed assignments, comparing the results under the same description of uncertainty of patients' demand.