AUTHOR=Torres Noelia , Trujillo Leonardo , Maldonado Yazmin TITLE=Modeling Uncertainty for the Double Standard Model Using a Fuzzy Inference System JOURNAL=Frontiers in Robotics and AI VOLUME=5 YEAR=2018 URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2018.00031 DOI=10.3389/frobt.2018.00031 ISSN=2296-9144 ABSTRACT=

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.