Optimizing construction time, cost, and quality: a hybrid AHP-NSGA-III model for enhanced multi-objective decision making

Abstract

In construction projects, optimizing time, cost, and quality (TCQT) simultaneously is a challenging task due to the inherent trade-offs between these objectives. This paper presents a hybrid optimization model that combines the analytical hierarchy process (AHP) with the non-dominated sorting genetic algorithm III (NSGA-III) to achieve a balanced approach. The model uses AHP to assign weights to activities and quality indicators, providing a structured framework for prioritizing project goals. NSGA-III is then employed to identify Pareto-optimal solutions, leveraging reference points to enhance diversity and distribution across the Pareto front. A comparative analysis with MOPSO, TLBO, and MOACO demonstrates that NSGA-III yields superior performance, generating more well-spread solutions with improved generational distance and hypervolume indicators. In a case study application, the model showcases its effectiveness in producing diverse, efficient project completion strategies that allow project managers to navigate TCQT trade-offs more effectively. Results indicate that NSGA-III’s reference direction-based approach offers substantial advantages over traditional algorithms, making it ideal for complex, multi-objective optimization in construction. This study provides a robust decision-making tool to enhance project efficiency and stakeholder satisfaction.