Integrated decision support system for optimizing time and cost trade offs in linear repetitive construction projects

Linear repetitive construction projects present unique challenges in optimizing both completion time and cost performance. Traditional scheduling techniques often struggle to effectively address these complexities. This paper aims to enhance project optimization by introducing a metaheuristic-based Time-Cost Trade-off (TCT) framework specifically designed for repetitive project environments. Unlike previous studies that focus solely on single-algorithm applications, this research evaluates two metaheuristic optimization strategies—Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)—within a consistent problem setting. The framework employs both algorithms, which are independently assessed for their effectiveness in tackling the Linear Repetitive Project Time-Cost Trade-off (LRPTCT) problem. The methodology utilizes task decomposition alongside the Line of Balance (LOB) scheduling technique, facilitating a more detailed and adaptable planning process. Each sub-task is systematically evaluated to identify the optimal construction method based on cost-time trade-offs, with scheduling constraints integrated into the fitness functions of both GA and PSO. Results from an in-depth case study reveal significant improvements in project efficiency. Specifically, GA achieved approximately a 3.25% reduction in direct costs, a 20% reduction in indirect costs, and a 7% reduction in total construction costs. In comparison, PSO demonstrated slightly superior cost performance, with a 4% reduction in direct costs and comparable reductions in indirect costs, along with a 20% decrease in total project duration. These findings highlight practical gains in resource utilization and scheduling efficiency. This study presents a structured, comparative analysis of GA and PSO within the LOB-based TCT framework, providing a replicable methodology for optimizing schedules in linear repetitive projects. By bridging the gap between traditional scheduling techniques and advanced optimization algorithms, this research contributes valuable insights for enhancing operational efficiency and informed decision-making in construction project management.