Optimizing Construction Project Plan Management Using Parameter-Adaptive Improved Genetic Algorithm

This study proposes an optimization method for construction project plan management using a parameter-adaptive improved genetic algorithm (IGA) combined with differential evolution (DE). The method addresses the challenges of inflexibility, inefficient resource allocation, and unscientific scheduling in traditional project management approaches. A multi-objective optimization model that balances project duration and resource utilization is developed. A case study demonstrated that the optimized methods reduced the project duration by up to 22.7% and the resource variance by up to 29.9% compared to the original plan. The proposed method enhances the flexibility and efficiency of construction project planning, contributing to both theoretical advancement in optimization algorithms and practical improvements in project management.