Modeling and simulation method of the emergency response systems based on OODA

Highlights

• Two kinds of emergency response frameworks are built based on OODA loop theory.

• Describe the emergency response mechanism based on OODA–DEVS theory model.

• Emergency response system on earthquake is modeling and simulating based on the mentioned mechanism.

Abstract

It is necessary to provide a set of scientific foundations to support the emergency response management. The emergency response process are described based on the ideas from OODA (Observe, Orient, Decide, Act) loop theory. Pointing at the difficulty in the described the cooperation in the emergency response process, the coupled OODA framework is built to analyze the interaction between the emergency response units. In order to demonstrate the emergency response mechanism in theoretic way, the simulation theory of DEVS (Discrete Event System Specification) is adopted to build up the simulator model of the basic OODA process framework. Utilize the coupled DEVS model to build the simulation coordinator of the coupled OODA process framework. The earthquake disaster response scenario in STAGE is built on the emergency response mechanism, and the emergency collaborative rescue process based on coupled OODA frameworks is adopted to build up the emergency response system, and the simulation results show reasonable.

Introduction

There are lots of countries like China, suffering from severe natural disasters and the others emergency disasters. It is very important to improve the emergency response effectiveness by mitigating the damage of the disasters. Due to the lack of enough emergency response information, lack of knowledge and theory of the emergency response mechanism, the decision-makers find it difficult to make a response decision. And therefore, the effectiveness of the emergency response will be affected greatly. For example, the weak points were exposed in the emergency response to the catastrophe of Wenchuan earthquake including: (1) the decision-making slowly, (2) less cooperation between different kinds of the rescue teams. Such shortcomings also existed in the similar disasters like Ya’an earthquake in south-west of China in 2013 and the super hurricane “Swallow” in Philippines in 2013. In fact, the Philippine government was scolded because of making emergency response slowly and carrying out rescue disorderly.

These examples show that it is very important to make scientific decisions based on the right mechanism of the emergency response system, so as to improve the emergency response capability.

On the emergency response, there are some references in recent years. For example, Hossain et al. [1], pointing to the emergency response to bushfire, propose an approach to map the collaboration network among emergency management personnel, and use Exponential Random Graph (ERG) models to explore the micro-level network structures of emergency management networks. Amailef and Lu [2] present a MERS ontology-supported case-based reasoning (OS-CBR) method to support emergency decision makers to effectively respond to emergencies. The OS-CBR approach includes a set of algorithms that have been successfully implemented in four components: data acquisition; ontology; knowledge base; and reasoning; as a sub-system of the MERS framework. There are also some other related papers in the area of Emergency Response Systems, such as Naderpour et al. [3], adopt the Bayesian networks and fuzzy logic system to support the operators to reduce the errors and improve the performance when confront with abnormal situations; Sharma et al. [4], research the emergency response plan proposed for petroleum storage sites based on e-ICS (electronic-Incident Command System (e-ICS)); Wang et al. [5], propose a method of generating task network for emergency response based on the snowball procedure and an associated method of analyzing task network based on social network analysis; emergency response covers all operational and procedural tasks that are conducted individually or collaboratively by qualified professionals with the goal of normalizing the situation after a disruption [6], [7]; Millner et al. [8] analyze the emergency response and preparedness for hazardous materials includes accidental releases to intentional releases through acts of terrorism.

There also were some related papers about decision support systems for emergency management, such as, Fogli and Guida [9] propose a novel knowledge-centered design methodology and demonstrated through the application in a concrete case study in the field of pandemic flu emergency management; Malizia et al. [10], develop an ontology to investigate different sources: accessibility guidelines, emergency response systems, communication devices and technologies, taking into account the different abilities of people to react to different alarms; Ref. [11] presents a decision support system for assessing alternative distribution routes in terms of travel time, risk and evacuation implications while coordinating the emergency response deployment decisions with the hazardous materials routes; Li et al. [12], propose a strategy to form a community-based virtual database to respond to emergencies in a fast and an effective manner by means of MCDM. Some paper [13] proposing a decision-making framework, uses failure mode effect and criticality analysis (FMECA) to evaluate the effectiveness of ERS to make improvements in oil spill emergency management. Huang et al. [14], characterize the humanitarian objectives of emergency resource allocation and distribution in disaster response operations. An integrated multi-objective optimization model that combines resource allocation with emergency distribution is developed, where a time space network is used to incorporate the frequent information and decision updates in a rolling horizon approach. Mohammadfam et al. [15], examine the cohesions status of the coordination within response teams in the emergency response team (ERT) in a refinery, and utilize cohesion indicators of social network analysis (SNA; density, degree centrality, reciprocity, and transitivity) to examine the coordination of the response teams as a whole network. Thorstensson et al. [16] think that the monitoring and documentation of the internal work and communication processes can increase the ability to investigate and understand cause–effect relationships between incoming field reports, operational procedures, decisions, commands and the rescue response in the field. They present a method and a software tool that enable an observer to monitor and record communication events in a command post staff. McMaster and Baber [17] make an investigation of command and control during Multi-Agency Operations, and elaborate on the known themes associated with multi-agency emergency response. The emergency information processing by a command post station is described by Detect–Assess–Decide–Act (DADA). The DADA loop is a similar framework of the commonly used Observe–Orient–Decide–Act (OODA) loop [18]. The parts of Observe and the Orient in OODA loop have some connection with the situation awareness (SA). Naderpour et al. [19] present an innovative cognition-driven decision support system called the situation awareness support system (SASS) to manage abnormal situations in safety–critical environments in which the effect of situational complexity on human decision-makers is a concern. As a result, a situational network modeling process and a situation assessment model that exploits the specific capabilities of dynamic Bayesian networks and risk indicators are first proposed to improve performance and to reduce error in managing abnormal situation. On the emergency risk evaluation, Zhang et al. [20], propose an extended fuzzy multi-criteria group evaluation method, which can deal with both subjective and objective criteria under multi-levels by a group of evaluators, for emergency management evaluation.

The above mentioned showed that, there are many methods and ways to support the emergency response. But few references provided the enough sound theory and method to analyze the mechanism of the emergency response process. So it is necessary to understand the emergency response mechanism which should include the processes of occurrence, development, and evolution and end, so as to get reasonable way to support the emergency leader to make decision. For this reason, the scientific emergency decision needs to analyze the emergency response law in emergency management.

In fact, the emergency response operations had already been a part of the non-war military action in the many country. The America Air Force colonel, named John Boyd, proposed the OODA (Observe, Orient, Decide and Act) combating loop theory. The OODA loop theory was first described the force to carry out the four basic tasks in the combat loop. The idea is worth learning. The emergency response in civil field can also be seen as the military operation in peace year, and it is a new idea to research the emergency response mechanism based on OODA loop.

In fact, it is very difficult to describe the mechanism of the emergency response. But we analyze the emergency response process based on the OODA cycle theory, and it is easy to master the mechanism of the emergency response. Such a research will be helpful for the evaluation and analysis of the response effectiveness to the emergency disaster.

And therefore, the arrangement in this research includes: firstly, build the emergency response mechanism framework; then use the DEVS (Discrete Event System Specification) to build up a set of theoretical description models for the emergency response frameworks based on OODA; finally, take an abrupt earthquake as the scenario, and carry out the simulation application based on the OODA–DEVS models, and demonstrate the feasibility of the simulation results.

The work in this research paper can be highlighted as three items:

• Two kinds of emergency response frameworks are built based on OODA loop theory.

• Describe the emergency response mechanism based on OODA–DEVS theory model.

• Emergency response system on earthquake is modeling and simulating based on the mentioned mechanism.