Large-Scale Asset Renewal Optimization Using Genetic Algorithms plus Segmentation

Abstract

Civil infrastructure assets require continuous renewal actions to modernize inventory and sustain operability. However, allocating limited renewal funds among numerous asset components represents a complex optimization problem. Earlier efforts using genetic algorithms (GAs) optimized medium-sized problems, yet exhibited steep performance degradation as problem size increased. In this research, data compression is first used to cluster and abstract the large data of a network-level problem. Optimizing compressed models, however, did not result in high quality solutions. To address large size problems, a GA with segmentation approach was introduced. Segmentation breaks down a large-scale network-level problem into segments, allocates budget based on the relative criticality of the segment, and combines the results of all segment optimizations. The proposed GA with segmentation mechanism has been tested on different sized problems and was able to optimize very large problems with no performance degradation. The proposed GA with segmentation method is simple and logical; furthermore, it can be used on variety of asset types to improve fund allocation for infrastructure renewal.