Abstract

In recent years, a variety of unconventional incidents of natural disasters, accidents, disasters, public health and social security and other fields occur fre-quently, the degree of harm is more and more large. During respond to such unconventional emergencies, an efficient emergency logistics and distribution is the key to improve disaster prevention and disaster relief, and is also an impor-tant indicator to measure the government's emergency management capability.

In response to such unconventional emergencies, the emergency logistics distribution is under a highly dynamic and uncertainty transport road network, due to the outbreak of the disaster and the unpredictability of its develop-ment. Since the traditional logistics distribution scheduling method is hardly work in such environment, a new tools should be studied to support decision making under the new environmental emergency logistics.

A real-time road network and path generation method for emergency lo-gistics distribution vehicles is proposed in this study to solve the above prob-lems. From the microscopic point of delivery vehicles, dynamically update of distribution vehicle navigation map is studied; From the global perspective of the emergency decision makers, the real time generation of distribution routing scheme is studied. Integrated the advantages of both to respond to the dramatic changes of external transport network and the uncertainty of road traffic caused by disasters.

From the microscopic point of delivery vehicles, an emergency logistics distribution vehicle navigation map multi-scale spatial data model is proposed in this study to generate real-time distribution vehicle navigation map, by which to improve the speed and accuracy of emergency logistics vehicle navigation path analysis. The study is carried out according to the idea of: “spatial relationship decomposition→the most relevant vertices selection→sub -network regenera-tion”. The science of system and social network analysis theories is introduced during the research work, focusing on following issues: the spatial relationship measurement, the multi -scale spatial data model for vehicle navigation, and the application in vehicle navigation. The detailed contents of the research is as follows：

（1）The research on the connectivity index to measure the importance of a vertex in a road network of emergency logistics distribution vehicle naviga-tion. A new connectivity index, which we called the relative connectivity coef-ficient, is proposed to measure the impact of a vertex to another in a net-work. The spatial relationship of a network can be decomposed to the network vertices by this index. A simplified method is designed to reduce the computa-tional complexity of the relative connectivity coefficient, which uses the shape of the sub -tree rooted by a vertex to evaluate its relative connectivity coeffi-cient.

（2）The research on multi-scale spatial data model based on generalized scale for vehicle navigation. The characteristics of real -time vehicle navigation are analyzed, and a multi-scale spatial data model based on generalized scale is proposed, which can generate sub-network to adapt different destination ver-tex set. A Principal-Component-Analysis-based method and an Analytic-Hierarchy-Process -based method are proposed to calculate the relative con-nectivity coefficient for multi destination-vertex set. Furthermore, the main i-dea of this multi-scale spatial data model is applied to a class of network sam-pling problem to reduce the computational complexity of network analysis.

（3）The research on the network decomposition method for emergency logistics distribution vehicle navigation maps. Computational power of onboard devices is too limited to processing spatial data of vehicle navigation maps. A net-work decomposition method based on the above mentioned multi-scale spatial data model is proposed to solve this problem. The vehicle navigation maps are decomposed into sub -maps in the monitoring center, and these sub -maps can be downloaded to the onboard devices. The most relevant elements to the destinations are extracted from the entire network to compose sub -maps, so that the computational complexity of network analysis on these sub networks can be reduced with less accuracy loss. This method is applied to a case of searching the shortest path in onboard devices. Experimental evaluation shows that this method can effectively control the accuracy loss caused by network decomposi-tions: there is only 13.85% accuracy loss while the sub network's size is re-duced to 20.12% of the original network, and the computational time is re-duced from second magnitude to 100 microsecond magnitude at the same time.

From the global perspective of the emergency decision makers, a scenario evolvement based representation method of road networks for emergency logis-tics and distribution is proposed in this study to generate real -time route scheme, by which to improve the reliability of emergency logistics under the uncertain external road traffic situation. The disaster chain is represented by sce-nario evolvement, which is embedded in the spatial data of road network for e-mergency logistics and distribution. The science of uncertain planning is intro-duced during the research work, focusing on following issues: scenario evolve-ment based representation method of road networks for emergency logistics and distribution, the route planning model for emergency logistics distribution under uncertainty, and the application in the South Snow disaster. The detailed con-tents of the research is as follows：

（1）The research on a scenario evolvement based representation method of road networks for emergency logistics and distribution. In emergency disaster e-mergency logistics and distribution, the development and evolution of disaster chains will lead to switching of logistics decision state space. A scenario evolve-ment based representation method for road networks is established in this paper to meet the characteristics of emergency logistics distribution, in which decisions has to be made under a variety of uncertain scenarios led by unconventional e-mergencies. A dynamic scenario tree is proposed in this method to describe the diffusion and migration patterns of disaster scenarios among the road network, which is dynamically updated with real-time traffic information of the distribu-tion vehicles.

（2）The research on a scenario evolvement based route planning model for emergency logistics distribution under uncertainty. A scenario evolvement based route planning model is established to deal with the uncertainty of road networks for emergency logistics and distribution. The actual path is embedded in a“per-fect path”in this model, which includes all the sections in a road network, and a Monte Carlo and genetic hybrid algorithm is designed to compare their perfor-mation of overall satisfaction. Its applicability is verified by an example of emer-gency logistics in Fujian Province under rainstorm.

（3）The research on a scenario evolvement based route planning model for emergency logistics distribution with the South Snow disaster diffusion simula-tion. A disaster diffusion simulation of 2008 Southern Snow Disaster is embedded into the above scenario evolvement based route planning model to handle the disaster diffusion over time. An example of emergency logistics in Hengyang demonstrates the effectiveness of the above method.

The research in this paper has promoted the interaction and inosculation of geographic information science, systems science, operations research and other disciplines theory and methods. It is the beneficial exploration for improving the real-time processing of the emergency logistics vehicle road network spatial da-ta. The research results have broad application prospects in emergency logistics, vehicle navigation and geographic information science, which will play an im-portant role in emergency logistics distribution vehicle real-time navigation and scheduling work.