BackgroundThis study examined the daily surgical scheduling problem in a teaching hospital. This problem relates to the use of multiple operating rooms and different types of surgeons in a typical surgical day with deterministic operation durations (preincision, incision, and postincision times). Teaching hospitals play a key role in the health-care system; however, existing models assume that the duration of surgery is independent of the surgeon’s skills. This problem has not been properly addressed in other studies. We analyze the case of a Spanish public hospital, in which continuous pressures and budgeting reductions entail the more efficient use of resources.MethodsTo obtain an optimal solution for this problem, we developed a mixed-integer programming model and user-friendly interface that facilitate the scheduling of planned operations for the following surgical day. We also implemented a simulation model to assist the evaluation of different dispatching policies for surgeries and surgeons. The typical aspects we took into account were the type of surgeon, potential overtime, idling time of surgeons, and the use of operating rooms.ResultsIt is necessary to consider the expertise of a given surgeon when formulating a schedule: such skill can decrease the probability of delays that could affect subsequent surgeries or cause cancellation of the final surgery. We obtained optimal solutions for a set of given instances, which we obtained through surgical information related to acceptable times collected from a Spanish public hospital.ConclusionsWe developed a computer-aided framework with a user-friendly interface for use by a surgical manager that presents a 3-D simulation of the problem. Additionally, we obtained an efficient formulation for this complex problem. However, the spread of this kind of operation research in Spanish public health hospitals will take a long time since there is a lack of knowledge of the beneficial techniques and possibilities that operational research can offer for the health-care system.Electronic supplementary materialThe online version of this article (doi:10.1186/1472-6963-14-464) contains supplementary material, which is available to authorized users.

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Introducción: El objetivo de este trabajo es profundizar en el conocimiento de la enfermedad periimplantaria y los tratamientos existentes descritos en la literatura. Material y método: Revisión bibliográfica de las enfermedades periimplantarias, basada en una búsqueda en bases de datos Pubmed, Medline y Cochrane Library, utilizando como palabras clave "periimplantitis, mucositis, tabaquism, oral microbiota, occlusal overload, surgical treatment, antimicrobial therapy, detoxification, regenerative therapy, bone defects". Desarrollo y discusión: Análisis de los factores de riesgo, el diagnóstico y el tratamiento de las enfermedades periimplantarias. Conclusiones: Los factores como el tabaco o una historia de periodontitis, junto con una mala higiene oral, son las principales causas de las enfermedades periimplantarias. Un correcto diagnóstico de la etiología, así como, la adecuada elección de la terapéutica, pueden detener el avance de la enfermedad periimplantaria. Tras el tratamiento realizado, será muy importante el control periódico y el mantenimiento de una correcta higiene oral.

The inbound logistics for feeding the workstation inside the factory represents a critical issue in the car manufacturing industry. Nowadays, this issue is even more critical than in the past since more types of cars are being produced in the assembly lines. Consequently, as workstations have to install many types of components, they also need to have an inventory of different types of components in a usually compact space. The replenishment of inventory is a critical issue since a lack of it could cause line stoppage or reprocessing. On the other hand, an excess of inventory could increase the holding cost or even block the replenishment paths. The decision of the replenishment routes cannot be made without taking into consideration the inventory needed in each station during the production time, which will depend on the production, sequence plan sent by the central office. This problem deals with medium-sized instances and it is solved using online solvers. The contribution of this paper is a MILP model for the replenishment and inventory of the components in a car assembly line.