Comparative analysis of relocation strategies for ambulances in the city of Tijuana, Mexico.

This paper analyzes the problem of locating ambulances for the Red Cross of Tijuana, Baja California, Mexico, considering demand changes over time, a problem that usually requires relocating ambulances at specific moments in time. This problem is usually solved using optimization models, seeking to maximize coverage and reduce the response time using an appropriate relocation strategy, while also minimizing the cost of relocating ambulances across the city. The goal of this work is to determine the benefits and costs, advantages and disadvantages, of using different strategies to solve this problem for the Red Cross of Tijuana. Therefore, different solution strategies are evaluated, all of them are based on the Double Standard Model (DSM) for ambulance locations. The first approach is to apply a robust version of the DSM, Robust DSM, that finds the best trade-off solutions across all possible time periods, or scenarios, throughout the day. The second approach is to apply the DSM to each scenario independently, and then perform relocations based on the different configurations of the ambulances in different scenarios. The final approach is to use an explicit relocation model, the multi-period DSM. The approaches are evaluated based on the percentage of double coverage, total number of relocations, relocation travel time, relocation travel distance and the financial cost of performing relocations. More than 13,000 calls for Emergency Medical Services (EMS) were analyzed, received by the Red Cross from August 2016 to April 2017, such that the results obtained are based on a comprehensive characterization of this real-world case study. Results show that while the relocation approaches do provide better overall coverage, for the Red Cross of Tijuana, an EMS provider with limited resources and funding, the increased coverage may not justify the additional costs.

Modeling Uncertainty for the Double Standard Model Using a Fuzzy Inference System.

This paper studies the issue of uncertainty in the ambulance location problem to cover the maximum number of demand points in a city. The work is based on the double standard model (DSM), a popular coverage model where two radii are considered to cover a percentage of the demand points twice. Uncertainty is introduced in the expected travel time between an ambulance and a demand point, before computing the optimal placement of ambulances in potential bases by solving the linear program posed by the DSM. The following three approaches are considered: (1) solving the DSM without uncertainty; (2) uncertainty in the travel time is based on triangular fuzzy set; and (3) a fuzzy inference system (FIS) with a rule base derived from the problem properties, which is the main contribution of this work. Results show that considering uncertainty can have a significant effect on the solutions for the DSM, with the solutions produced with the FIS approach achieving a higher total coverage of the demand. In conclusion, the proposed strategy could provide a reliable and effective tool to support decision making in the ambulance location problem by considering uncertainty in the ambulance travel times.

Optimizing the location of ambulances in Tijuana, Mexico.

In this work we report on modeling the demand for Emergency Medical Services (EMS) in Tijuana, Baja California, Mexico, followed by the optimization of the location of the ambulances for the Red Cross of Tijuana (RCT), which is by far the largest provider of EMS services in the region. We used data from more than 10,000 emergency calls surveyed during the year 2013 to model and classify the demand for EMS in different scenarios that provide different perspectives on the demand throughout the city, considering such factors as the time of day, work and off-days. A modification of the Double Standard Model (DSM) is proposed and solved to determine a common robust solution to the ambulance location problem that simultaneously satisfies all specified constraints in all demand scenarios selecting from a set of almost 1000 possible base locations. The resulting optimization problems are solved using integer linear programming and the solutions are compared with the locations currently used by the Red Cross. Results show that demand coverage and response times can be substantially improved by relocating the current bases without the need for additional resources.