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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.

This article presents a new algorithm for industrially sized problems that, because of the large number of tasks to schedule, are either intractable or result in poor solutions when solved with full-space mathematical programming approaches. Focus is set on a special type of multistage batch plant featuring a single unit per stage, zero-wait storage policies, and a single transportation device for moving lots between stages. The algorithm incorporates a mixed-integer linear programming (MILP) continuous-time formulation and a discrete-event simulation model to generate a detailed schedule. More precisely, three stages are involved: (i) finding the best processing sequence, assuming that the transportation device is always available; (ii) generating a feasible schedule, taking into account the shared transportation resource; (iii) improving the schedule through a neighborhood search procedure. Relaxed and constrained versions of the full-space MILP are involved in stages (i) and (iii) with the simulation model taking care of stage (ii). Several examples are solved to illustrate the capabilities of the proposed method with the results showing better performance when compared to other published approaches. The balance between solution quality and total computational effort can easily be shifted by changing the number of lots rescheduled per iteration.