AbstractThis research is motivated by the lesson learnt from the 2004 Tsunami and the need to develop a post-tsunami aid delivery algorithm. To our knowledge, emergency planning for tsunamis in Thailand developed after the 2004 event that included only tsunami warning systems and evacuation procedures. However, there was no consideration of utilising an aid delivery algorithm, which critically depends on estimates of the affected population. With the use of geographic information systems (GIS), there has been renewed interest in spatial population estimation as well as map visualization. The main aim of this research, therefore, is to develop a decision support system (DSS) by integrating GIS and vehicle routing algorithm to facilitate decision-making for the purpose of being able to plan and operate humanitarian relief logistics efficiently and effectively.
To this end, this research encompasses three main development phases. Firstly, the algorithm of Affected Population Estimation (APE) is developed. A multi-stage spatial interpolation is proposed for estimating the affected populations using GIS functionalities in ArcGIS 10.3. A different perspective of a dasymetric-mapping technique using a population-weighted technique coupled with remote sensing data is presented in the first stage, while the street-weighted methods are addressed in the following stages. The results in each target zone include a range of numbers affected by the tsunami inundation.
Humanitarian Logistics Optimization (HLO) is the second phase of this research. In this phase, the Capacitated Vehicle Routing Problem (CVRP) with simulated demand is studied. The solutions generated from the population-estimation module are transferred to this phase in the form of estimated demand, which has been assigned to each evacuation location. This study phase aims to solve the CVRP with simulated demand for humanitarian logistics. Being the combinatorial problem, the CVRP is modelled and solved by the Clarke and Wright Saving heuristic (CWS). Then, the quantities of demand are simulated using a Monte Carlo technique in order to obtain the most possible outcomes based on the seasonal-specific parameters. Together with the transport resource variables identified in the problem, the CVRP solutions include not only a set of routes with minimum cost but also transport resource efficiency and route priority. The transport resource ratio determines whether the given transport resources are sufficient for the operation deadline, while route priority is useful for decision-making in case the transport resources are limited.
In the last phase, the GIS-based DSS is developed based on the algorithms proposed in the first two phases. Hence, an application is programmed by combining those two algorithms, all of which are based on GIS utilization. The graphical user interface (GUI) is designed for very intuitive use, with the user only inputting a set of data and parameters. The main advantage of this GIS-based DSS is that the decision maker does not require special skills in GIS and in operating the complex processes in the ArcGIS environment. This innovative tool straightforwardly produces not only numerical solutions for the decision maker, but also visualisation maps on the GUI as well as in the ArcGIS system.
A case study of Phuket effectively illustrates the proposed DSS. The outcomes can be used as a key decision-making factor in planning and managing humanitarian relief logistics before and during the disaster response phase to improve their performance the next time around.
|Date of Award||Sep 2018|
|Supervisor||Graham Wall (Supervisor) & Dylan Jones (Supervisor)|