An Optimization Model for Emergency Vehicle Location and Relocation with Consideration of Unavailability Time

Abstract

The main purpose of emergency medical services is providing fast medical care, as well as transporting patients to the hospital, in the shortest possible time. Healthcare managers try to improve healthcare systems through reducing the response to demand time. In this paper, we seek to propose an optimization model in order to cover as much demand as possible in the shortest possible time using the available ambulance fleet. To do so, considering the response and service time, amount of demand during the time periods, limitation in the number of available emergency vehicles and the capacity of ambulance stations, we have proposed a mixed integer linear programming optimization model, aiming to minimize the total response time. In this paper we take into account fleet relocation and unavailability time, the time interval in which the vehicle is on its way or doing a service at a demand point. Then, a sensitivity analysis is conducted on the model by manipulating the parameters, so as to observe the effects on the outputs. In order to evaluate the model, several artificial test problems were generated and solved. The results depict the capability of the proposed model in dealing with emergency cases.

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

“…Enayati et al [110] proposed a real-time optimization to tackle managerial decisions related to ambulance fleets. In their model, the authors implement two linear programming models in series.…”

“…For example, Tsai et al [89] use time series smoothing to tackle stochasticity to make better relocation decisions. Other researchers deal with demand stochasticity with hypercube models like in [100,110,114,115,126,128]. Other studies deal with stochasticity in travel times using machine learning [130], as Olave-Rojas and Nickel [131] have previously done.…”

“…However, current computational technology, information technology, and geographic information systems motivate a posteriori type tools development. Several authors (Yue et al [134], Majzoubi et al [135], Mason [83], Billhardt et al [101], Jagtenberg et al [65], Lam et al [105], Van Barneveld, [106,109]; Enayati et al [110], Sudtachat et al [67], Yoon and Albert [136], Roa et al [75], Sun et al [137], Yuangyai et al [138], Nilsang et al [139], van Buuren et al [140], Tsai et al [89]) worked on real-time response tools, which, in the current context, are selfevident as a promising research development line in the future of these tools.…”

“…Some authors have considered objectives integration to address the problem as a multiobjective one. Among these studies are [76,82,88,95,110,136,141,144,145]. All of them used different ways to weigh the objective functions.…”

A simulation-optimization algorithm for return strategies in emergency medical systems

“…Furthermore, they used a discrete-event simulation approach, and concluded that their innovative strategy would reduce the probability of tardiness. Firooze et al 10 presented a multi-period dynamic model, in which the dispatch strategies for idle ambulances and the redeployment strategies for ambulances having completed their missions are defined. The proposed model minimizes response time by considering the proper redeployment of the ambulances.…”