Applying genetic algorithm techniques for time-cost optimization

Completing a construction project with the least time and cost is critical in a competitive environment. However, shortening the duration of any construction activity usually requires the engagement of additional labour and plant or the adoption of alternative construction techniques, and these usually add extra cost liabilities to the contractor. To minimize the total costs associated with schedule compression, contractors are often urged to establish the optimal time-cost relationships for construction activities when planning decisions are made. Several analytical models have been developed for time-cost optimization. Despite that, difficulties are still being encountered in construction Time-Cost Optimization (TCO), as there is a lack of unique solutions for integrating constraints associated with the time and cost requirements. Being a powerful tool to locate the global optimum (rather than local optimum), the Genetic Algorithms (GA) could be used to establish the fitness of solutions by evaluating the objective function and its constraints. In this paper, the analytical power of the GA is compared with other techniques proposed for TCO. The results indicate that the GA techniques could generate the most optimal outcome for construction TCO, especially when the project is large and/or complex.