This is a repository copy of A probabilistic patient scheduling model for reducing the number of no-shows.

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We tackle the problem of finding, for each network within a collection, the shortest path between two given nodes, while not exceeding the limits of a set of shared resources. We present an integer programming (IP) formulation of this problem and propose a parallelizable matheuristic consisting of three phases: 1) generation of feasible solutions, 2) combination of solutions, and 3) solution improvement. We show that the shortest paths found with our procedure correspond to the solution of the Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) and that a particular case of the RCMPSP occurs in Air Traffic Flow Management (ATFM). Our computational results include finding optimal solutions to small and medium-size ATFM instances by applying Gurobi to the IP formulation. We use those solutions to assess the quality of the output produced by our proposed matheuristic. For the largest instances, which correspond to actual flight plans in ATFM, exact methods fail and we assess the quality of our solutions by means of Lagrangian bounds. Computational results suggest that the proposed procedure is an effective approach to the family of shortest path problems that we discuss here.

It is well known that excessive rescue times after traffic accidents negatively affect the health of those injured. There is a need to quantitatively measure the impact of unexpected events like ambulance availability, weather, floating population and congestion in those rescue times. A family of indicators based on isochrones is disguised and proposed to understand the risk of the whole population as the probability of not being assisted on time. Indicators of health risk for local towns are also defined. The indicators are calculated using a simulation model and visualized in web format. The framework of analysis is validated using Ávila (Spain) and the problem of the optimal deployment of ambulances as a test-bench.