A New Optimization Approach to Resource Distribution Using Semi-Supervised Learning Graphs

Abstract

An effective Post-Disaster Management System (PDMS) will result in distribution of emergency resources such as, hospital, storage and transportation in a reasonable and equitable manner. This study starts with semi-supervised learning (SSL) based graph system to provide post-disaster path optimizations. Next, the graph-based resource is converted to a directed graph resulting in an adjacent matrix. Decision information is provided in two ways: clustering operation and graph-based semi-supervised optimization. The PDMS in this study incorporates a path optimization algorithm based on Ant Colony Optimization (ACO) that results in costeffective resource distribution. Simulation results demonstrate the effectiveness of the proposed methodology by comparing ACO with clustering based algorithms of tour improvement algorithm (TIA) and Min-Max Ant System (MMAS).