Utility of genetic algorithms for solving large scale construction time/cost trade-off problems

The Time/Cost Trade-off (TCT) problem has long been a popular optimization question for construction engineering and management researchers. The problem manifests itself as the opti- mization of total costs of construction projects that consist of indirect project costs and individual activity costs. The trade-off occurs as project duration and, as a result, indirect project costs de- crease with reduced individual activity duration. This reduction in individual activity duration is achieved by increasing resource allocation to individual activities, which increases their costs to completion. Historically, metaheuristic solutions have been applied to small scale problems due to computational complexities and requirements of larger networks. In this paper, we demonstrate that the metaheuristic approach is highly effective for solving large scale construction TCT problems. A custom Genetic Algorithm (GA) is developed and used to solve large benchmark networks of up to 630 variables with high levels of accuracy (