Multi-Agent Simulation of Allocating and Routing Ambulances Under Condition of Street Blockage After Natural Disaster

Abstract

ABSTRACT:In response to natural disasters, efficient planning for optimum allocation of the medical assistance to wounded as fast as possible and wayfinding of first responders immediately to minimize the risk of natural disasters are of prime importance. This paper aims to propose a multi-agent based modeling for optimum allocation of space to emergency centers according to the population, street network and number of ambulances in emergency centers by constraint network Voronoi diagrams, wayfinding of ambulances from emergency centers to the wounded locations and return based on the minimum ambulances travel time and path length implemented by NSGA and the use of smart city facilities to accelerate the rescue operation. Simulated annealing algorithm has been used for minimizing the difference between demands and supplies of the constrained network Voronoi diagrams. In the proposed multi-agent system, after delivering the location of the wounded and their symptoms, the constraint network Voronoi diagram for each emergency center is determined. This process was performed simultaneously for the multi-injuries in different Voronoi diagrams. In the proposed multiagent system, the priority of the injuries for receiving medical assistance and facilities of the smart city for reporting the blocked streets was considered. Tehran Municipality District 5 was considered as the study area and during 3 minutes intervals, the volunteers reported the blocked street. The difference between the supply and the demand divided to the supply in each Voronoi diagram decreased to 0.1601. In the proposed multi-agent system, the response time of the ambulances is decreased about 36.7%.

Smart Cities and Healthcare: A Systematic Review

“…From the retrieved 44 articles, 34 were published in conference proceedings and only 10 were published in scientific journals [16][17][18][19][20][21][22][23][24][25]. After the respective analyses, these articles were divided into the following application domains: population surveillance; active ageing; healthy lifestyles; support to disabled people; response to emergencies; care services organization; and socialization.…”

“…The 10 articles related to the response to emergencies focused on determining emergency situations [51], autonomous vehicles [22], intelligent management of emergency vehicles [23,52,53], and emergency management applications [24,[54][55][56][57].…”

“…During emergency situations, one important concern is the dispatch of emergency vehicles. In this context, three of the included articles were related to an intelligent traffic management application to optimize the utilization of emergency vehicles: [23] presented a multi-agent system to support rescue operations by integrating the allocation of emergency vehicles to the locations of the wounded, the way finding of emergency vehicles, and the facilities of a smart city; [52] reported on an algorithm that primarily focused on dynamically determining the green light duration, but that was also able to handle the management of emergency vehicles; and [53] considered the London Ambulance Service as a case study to introduce an enhanced routing and dispatch method that combined the concurrent assignment and redeployment of units.…”

“…Additionally, several articles reported on the use of data analytic tools to process the collected data [19,23,29,31,[52][53][54][56][57][58][59]: recommendations of the best routers to individuals [19]; support of rescue operations by integrating smart city facilities for a better allocation of emergency vehicles to the wounded locations [23]; genome-wide association studies to better understand the link between genetic traits and disease [29]; a structured approach to collect and re-use sensor fitness data [31]; dynamically determining the duration of the green lights to better handle the management of emergency vehicles [52]; an enhanced routing and dispatch method that combines the concurrent assignment and redeployment of emergency vehicles [53]; semantic tools supporting the automatic creation of conceptual models for the creative design of emergency management scenarios [54]; crowd sourcing of the multitude of human and physical sensing resources that can generate data regarding incidents [56]; planning response to emergencies [57]; the prediction of needs in home care services [58]; and the design of value-infused citizen-focused services in smart cities [59]. Table 1.…”

ARPS: A Framework for Development, Simulation, Evaluation, and Deployment of Multi-Agent Systems

“…Also, during the simulation, it is possible to create unexpected scenarios to see how the agents perform. This can be desirable in areas that already employ ABM for resource management during a disaster, such as [30], that could be extended to have software agents that could direct resources where it is necessary. We are aiming to combine the characteristics of both simulation and development to offer an alternative for creating solutions in resource management using MAS.…”