In the practice of construction engineering, the key to measuring the success or failure of a project is to effectively balance multiple objectives such as project duration, cost, and quality. However, common single objective optimization methods or basic heuristic algorithms in the past often struggle to properly handle conflicting relationships between multiple objectives and adapt to the constantly changing constraints during project implementation. Therefore, this study proposes an improved genetic algorithm (IGA). This algorithm integrates two core mechanisms: one is to continuously maintain the Pareto front, and the other is to adaptively adjust dynamic constraints, thereby integrating construction process scheduling, resource allocation, and process selection into the same framework for collaborative optimization. Based on data from a real residential project, experimental analysis was conducted, and the results showed that compared with the original particle swarm optimization algorithm (PSO), simulated annealing algorithm (SA), and traditional genetic algorithm (GA), IGA can reduce the average construction period by 12%, reduce costs by 8%, and promote a 15% increase in quality pass rate. In addition, the Pareto solution set obtained by this algorithm has improved diversity by 20%, and its robustness has also been enhanced by 30% in the face of dynamic disturbances such as weather changes and resource supply fluctuations. This study provides a globally efficient and practical solution for multi-objective optimization problems in the field of construction, which helps promote the development of project management towards higher efficiency, economy, and quality.
Fu et al. (Thu,) studied this question.