Ambulance planning involves decisions to be made on different levels. The decision for choosing base locations is usually made for a very long time (strategic level), but the number and location of used ambulances can be changed within a shorter time period (tactical level). We present possible formulations for the planning problems on these two levels and discuss solution approaches that solve both levels either simultaneously or separately. The models are set up such that different types of coverage constraints can be incorporated. Therefore, the models and approaches can be applied to different emergency medical services systems occurring all over the world. The approaches are tested on data based on the situation in the Netherlands and compared based on computation time and solution quality. The results show that the solution approach that solves both levels separately performs better when considering minimizing the number of bases. However, the solution approach that solves both levels simultaneously performs better when considering minimizing the number of ambulances. In addition, with the latter solution approach it is easier to make a good trade-off between minimizing the number of bases and ambulances because it considers a weighted objective function. However, the computation time of this approach increases exponentially with the input size whereas the computation time of the approach that solves both levels separately follows a more linear trend.
|Place of Publication||Eindhoven|
|Publisher||TU Eindhoven, Research School for Operations Management and Logistics (BETA)|
|Number of pages||27|
|Publication status||Published - Oct 2013|
|Name||Beta working paper|
|Publisher||University of Eindhoven, Beta Research School for Operations Management and Logistics|
- Integer Programming
- Ambulance planning
- Local search
- Stochastic programming
van Essen, J. T., Hurink, J. L., Nickel, S., & Reuter, M. (2013). Models for ambulance planning on the strategic and the tactical level. (Beta working paper; No. WP-434). Eindhoven: TU Eindhoven, Research School for Operations Management and Logistics (BETA).