Construction time-cost trade-off analysis using ant colony optimization algorithm

Time-cost trade-off problem (TCTP) is one of the most important aspects of construction project planning and control. Construction planners must select appropriate resources, including crew size, equipment, methods and technologies to perform tasks of a construction project. In general, there is a trade-off between time and cost to complete a task; the less expensive the resources, the long it takes. Such problems are difficult to solve because they do not have unique solutions. Then it is essential for contracting organizations to carefully evaluate various approaches to attaining an optimal time-cost equilibrium. Existing methods for time-cost trade-off analysis can be categorized into three areas: mathematical programming models, heuristic methods and global search algorithms. Mathematical programming models and heuristic methods are not efficient enough for large scale networks (hundreds of activities or more). During the last decade, evolutionary methods such as genetic algorithms (GAs) and improved GAs have been used extensively for the construction time-cost trade-off analysis. More recently, ant colony optimization algorithms (ACOA), which are evolutionary methods based on the foraging behavior of ants, have been successfully applied to a number of benchmark combinatorial optimization problems. The multiobjective model for TCTP proposed in this paper is powered by techniques using ACOA. One of the main goals of this paper is to investigate the applicability of an alternative intelligent search method in time-cost optimization. By incorporating with the, modified adaptive weight approach (MAWA), the proposed model finds out the optimal solution and defines the Pareto front. The concept of the ACOA-based multiobjective TCTP model is implemented by a computer program, and a test example is conducted. The results indicate that the ACOA is proven to be an efficient means for searching optimal solutions in time-cost trade-off problems, and the model could assist decision-makers in concurrently arriving at an optimal project duration and total cost.