Extended Abstract
Introduction
Disasters such as earthquakes have always been a serious threat to human life in urban environments.
People have always sought to reduce the financial and human damages caused by such disasters.
Large scale earthquakes and rapid changes in the environment make the people unable to deliver the optimal solution to save lives and minimize damages. The earthquake has destroyed streets, roads and other infrastructures, and also causes fire. Fires which are started by earthquake, destroy homes in the city. During different periods of time, earthquake damages on Iranian society in social and economic fields are clear, therefore, dealing with the crisis in the form of a proper management and optimization is absolutely necessary. Complicated access to the earthquake-stricken areas, is always along with the difficulty of the rescue operations. Management structures can only overcome the prevailing political situation in these difficult circumstances that the acceptable level of cohesion and flexibility are paramount. The importance of intelligent systems that can help rescue the human species is very obvious.
 
Materials & Methods
Agent-based Modeling (ABM) is a new approach to the development of simulation tools for complex phenomena in different areas such as natural disasters, biological studies, and earthquake rescue. This paper presents a simulation system for the search and rescue (SAR) operation using Geospatial Information System (GIS), multi-agent systems (MAS) and the concept of integration for dynamic task allocation.
Due to the flexibility of the agent based systems and the possibility of combining space and time, MASs can be a powerful tool in the simulation of rescue operations and strategic management issues. These systems can simulate all factors in earthquakes such as people, robots, helicopters, and vehicles to communicate and cooperate with each other to solve the distributed problems.
In several studies, the high capability of using agent-based Modeling structures to model human behavior as a part of an environment to coordinate the rescue operation is referenced. The use of the agent-based Modeling and the possibility of combining flexibility with respect to location and time of the simulation can be a powerful tool in their search and rescue operations and strategic management issues. There are many reasons to use multi-agent systems to manage the crisis. Multi agent systems make it possible to simulate the demolition of buildings and homes, the fire, firefighters' activities, urban infrastructure damages, injured and displaced, and the victims, so they can find optimal strategies for search and rescue operations in large-scale accidents and crisis management performance in which multi-factor systems are used. Multi-agent systems are allowed to participate in environment to cooperate or compete with the environment. Multi-agent systems are targeted complex systems with an emphasis on the interaction between agents. They can break complex systems into sub-systems and other simple factors in environments. Various studies have stated that MASs possess high potentials for natural disaster management, from rescue operation to locating the positions of injured persons.
The main object of this paper is to use multi-agent systems to simulate activities and increase the efficiency of rescue groups. This research tries to offer a way to find relationship between the number of search agents and rescue agents with regard to the maximum number of surviving people. This system can be used for managing and decision-making before the earthquake.
 
Results & Discussion
Evaluation of the developed system took place in a part of Region 3 in Tehran. The proposed system consists of three parts: 1) Modeling environment and working groups with the use of GIS, analysis of the search operation by multi-factor system and visualization of the results. Therefore, environment has been modelled using spatial data, and the amount of space that each agent must search, is assigned for each agent. 2) Then, each of the search agents uses the ant colony algorithm for sequencing tasks in order to find a near-optimal solutions to look for environment. 3) The injuries that are found by search agents are assigned to rescue agents through net contract and then operation is executed.
 
Conclusion
The Result of this research is shown in the form of a diagram which highlights the relationship between the number of search agents and release agents (according to the number of survivors, and is done). The result offers a model in finding the number of people needed for rescue operations in different parts of the city.