An Integrated Decision Support System for Optimizing Time-Cost Trade-offs in Linear Repetitive Projects

The primary aim of this paper is to minimize the overall completion time of linear repetitive projects while simultaneously reducing direct and indirect costs by employing predefined construction methods. To address these challenges, the study proposes a dual-optimization framework that integrates Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The methodology involves decomposing repetitive tasks into sub-tasks, enabling a more detailed and feasible Time-Cost Trade-off (TCT) analysis, which is further combined with the Line of Balance (LOB) methodology. A comparative analysis between GA and PSO highlights their respective strengths and effectiveness, an area previously underexplored in the literature. The findings reveal that the proposed framework significantly reduces costs and project durations. The GA approach achieves reductions of approximately 3.25% in direct costs, 20% in indirect costs, and 7% in total construction costs, while PSO demonstrates slightly better cost efficiency with a 4% reduction in direct costs and similar reductions in indirect costs. Both methods deliver a 20% reduction in project completion time, showcasing their effectiveness in streamlining construction processes. This paper contributes to the field by presenting the GA-PSO-based Linear Repetitive Project Time-Cost Trade-off (LRPTCT) model, integrating TCT with LOB, and offering a novel solution to the complexities of linear repetitive projects. Furthermore, its comparative analysis between GA and PSO provides valuable insights for optimizing construction project management practices.