Towards optimising construction-method selection strategies using genetic algorithms

Although there is a proliferation of techniques for resource-allocation problems in construction, ranging from mathematical programming to knowledge-based expert systems, there still exists a need for the use of more efficient approaches in practice. This paper presents a research study that investigates the application of genetic algorithms (GAs) to the multidimensional problem. The objective is to investigate the use of GAs for both a numerical function optimisation and a combinatorial search problem within the framework of a decision-support system (DSS). A hybrid GA system was designed for construction-resource selection, and a genetic model that represents the problem and solution space was built into the system. A genetic state-space search (GSSS) technique for multimodal functions was used to evaluate the cost profiles that resulted from different combinations of tasks and resources. The study indicates that GA systems have huge potential applications as DSS component(s) in construction-resource assignment. The results also highlighted that GAs exhibit the chaotic characteristics that are often observed in other complex non-linear dynamic systems. The paper discusses the genetic model for the problem, and the empirical results obtained. Recommendations are given on how to achieve improved results in adapting GAs for construction-resource optimisation problems.