DTMM:面向疏散灾害管理的应急物流优化调度模型

DTMM:Emergency logistics optimization scheduling model for evacuation disaster management

文中重点关注灾后快速、协调的人员大规模疏散,以有效减少损失。有效的疏散调度是研究和开发的一项重要任务和优先事项。该系统采用动态跟踪数学模型(DTMM)中的场景建立,此场景是为移动云计算平台开发的。DTMM是一种针对灾难人口密度的面向疏散优化调度模型(EOOSM)。这包括用于数据收集的移动物联网(IoT)接口以及云处理与分析后端系统。在IoT/Fog灾害管理的典型场景下开发解决方案,并提出了一个使用人工场效(AFE)的IoT应用方法,将DTMM算法作为疏散调度的核心。人工场效(AFE)被设计成一个IoT系统,适用于大规模人群的快速疏散,可以根据势场的梯度方向自动确定疏散路径。在AFE的基础上,引入EOOSM作为人工场效,并带有AFE-AR关联。AFE-AR指挥被疏散者与同一个避难场所保持联系,安抚被疏散者,并尽可能进行人道主义疏散。

This research 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.