DTMM: Evacuation oriented optimized scheduling model for disaster management

Abstract

Over recent years there have been different types of natural hazard and man-made disasters which have been diverse and difficult to manage with heavy casualties. In this research, we are focusing on the rapid and coordinated evacuation of large populations after disasters to effectively reduce damage. An important task and a priority of the research and development is effective evacuation scheduling. We develop the system using it scenario which uses the Dynamic Tracking Mathematical Model (DTMM) for mobile cloud computing platform developing. DTMM is an Evacuation Oriented Optimized Scheduling Model (EOOSM) for disaster population densities. This includes the mobile (IoT) interface for data collection and the cloud processing and analytics back end system. We develop our solution based in the typical scenario of IoT/Fog disaster management, and we propose an IoT application that uses the Artificial Field Efficiency (AFE), the core DTMM algorithm for evacuation scheduling. AFE is designed as an IoT system, which is suitable for rapid evacuation of large populations and can decide the evacuation path automatically by gradient direction in the potential region. People are usually in distress, which quickly triggers evacuation confusion and leads to a secondary disaster. On the basis of the AFE, the Evacuation Oriented Optimized Scheduling Model (EOOSM) is introduced as the Artificial Field Efficiency with Attraction of the Relationship AFE-AR. AFE-AR directs evacuees in relation to the same shelter, calm evacuees and perform humanitarian evacuation to the extent possible cases.