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Integrated decision support system for optimizing time and cost trade offs in linear repetitive construction projects
Ahmed Gouda Mohamed1, Ali Hassan Ali2,3, Ahmed Adel Abdelhady2
1Construction Engineering and Management Department, Civil Engineering, Faculty of Engineering, The British University in Egypt (BUE), El Sherouk City, 11837, Cairo, Egypt. ahmed.ghanem@bue.edu.eg.
Scientific Reports
|June 20, 2025
Summary
This study introduces a metaheuristic framework for optimizing repetitive construction projects, comparing Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for time-cost trade-offs. Both algorithms improved efficiency, with PSO showing a slight edge in cost reduction and duration decrease.
Area of Science:
- Construction Management
- Operations Research
- Computational Intelligence
Background:
- Linear repetitive construction projects face complex time and cost optimization challenges.
- Traditional scheduling methods are often inadequate for these complexities.
- Metaheuristic approaches offer potential for enhanced project optimization.
Purpose of the Study:
- To introduce and evaluate a metaheuristic-based Time-Cost Trade-off (TCT) framework for repetitive construction projects.
- To comparatively assess the effectiveness of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for the Linear Repetitive Project Time-Cost Trade-off (LRPTCT) problem.
- To integrate task decomposition and Line of Balance (LOB) scheduling within the TCT framework.
Main Methods:
- Developed a TCT framework utilizing GA and PSO for LRPTCT problem-solving.
- Employed task decomposition and Line of Balance (LOB) scheduling techniques.
- Integrated scheduling constraints into the fitness functions of GA and PSO for optimization.
Main Results:
- GA yielded reductions in direct (3.25%), indirect (20%), and total (7%) construction costs.
- PSO achieved a 4% reduction in direct costs and a 20% decrease in total project duration.
- Both algorithms demonstrated significant improvements in resource utilization and scheduling efficiency.
Conclusions:
- The metaheuristic-based TCT framework effectively optimizes schedules for linear repetitive projects.
- GA and PSO offer viable, comparative strategies for addressing LRPTCT challenges.
- This research provides a replicable methodology for enhancing construction project management and decision-making.
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