Paper Info
Title
Modeling Uncertainty for the Double Standard Model Using a Fuzzy Inference System.
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.
Year
DOI
Venue
2018
10.3389/frobt.2018.00031
FRONTIERS IN ROBOTICS AND AI
Keywords
Field
DocType
ambulances,emergency medical services,bases,double standard model,triangular fuzzy set,fuzzy inference system
Mathematical optimization,Computer science,Fuzzy set,Artificial intelligence,Linear programming,Travel time,Machine learning,Fuzzy inference system
Journal
Volume
ISSN
Citations 
5.0
2296-9144
0
PageRank 
References 
Authors
0.34
9
3
Name
Order
Citations
PageRank
Noelia Torres100.34
Leonardo Trujillo24111.33
Yazmin Maldonado3936.20