P. Kolios, G. Milis, C. Panayiotou, T. Staykova and H. Papadopoulos, "A resource-based decision support tool for emergency response management," 2015 2nd International Conference on Information and Communication Technologies for Disaster Management (ICT-DM), Rennes, 2015, pp. 159-165. doi: 10.1109/ICT-DM.2015.7402032 keywords: {decision support systems;emergency management;mathematical programming;computer-aided decision support system;emergency response management;mathematical program;medical resources;real-time resource allocation;resource-based decision support tool;Decision support systems;Emergency services;Hospitals;Receivers;Resource management;Vehicles;Emergency management;decision support;medical resource allocation;network flows}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7402032&isnumber=7402014 © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, in-cluding reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to serv-ers or lists, or reuse of any copyrighted component of this work in other works.

In an emergency event, the efficient and effective management of medical resources reduces response times and better meets the patient's needs. However, the heterogeneity of types of resources (e.g., crews and expertise levels, vehicles, hospital capacity) and needs of patients (e.g., trauma status and type, location) makes efficient management of resources a very challenging task. In an effort to improve the management of medical resources during emergencies, this work designs and proposes a decision support tool that can be used for efficient real-time resource allocation. First, a network model is derived to capture the resources' flow. Thereafter, a mathematical program is formulated so as to allow optimization of the resources' allocation, and an online algorithmic implementation is provided to enable real-time execution within, for example, a computer-aided decision support system. Extensive simulations are also conducted under various scenario settings to experiment with the proposed tool and to demonstrate the potentially achievable efficiency gains.