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<p>Optimizing the trade-off between time, cost, and quality in construction projects is a critical challenge in project management. Conventional time-cost trade-off (TCTO) models often overlook the impact of construction quality, leading to suboptimal decisions. This study presents a novel hybrid approach that integrates the analytic hierarchy process (AHP) and Opposition-based learning non-dominated sorting genetic algorithm III (OBL-NSGA III) to enhance time-cost-quality trade-off (TCQT) optimization. AHP is used to derive relative weights for project activities and quality indicators, enabling structured decision-making, while OBL-NSGA III facilitates efficient search space exploration and Pareto front generation. A real-world case study demonstrates the model’s capability to generate diverse and high-quality non-dominated solutions. Correlation analysis among time, cost, and quality components validates the model’s logical consistency. Sensitivity analysis reveals the robustness of results under varying parameters. The weighted sum method (WSM) is employed to identify an optimal solution from the Pareto front. Comparative evaluation against existing algorithms (MOPSO, MOACO, LHS-NSGA III) confirms superior performance in convergence, diversity, and hypervolume. The proposed model provides project managers with a comprehensive and intelligent decision-support framework, ensuring optimized trade-offs and quality-integrated scheduling in complex construction environments.</p>

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Tri-objective optimization of time, cost, and quality in construction management using analytic hierarchy process and OBL-enhanced NSGA-III

  • 期刊论文

  • 发表日期：

    2025年10月27日

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  • Meghraj Kaurav,
  • Manoj Sharma

摘要

Optimizing the trade-off between time, cost, and quality in construction projects is a critical challenge in project management. Conventional time-cost trade-off (TCTO) models often overlook the impact of construction quality, leading to suboptimal decisions. This study presents a novel hybrid approach that integrates the analytic hierarchy process (AHP) and Opposition-based learning non-dominated sorting genetic algorithm III (OBL-NSGA III) to enhance time-cost-quality trade-off (TCQT) optimization. AHP is used to derive relative weights for project activities and quality indicators, enabling structured decision-making, while OBL-NSGA III facilitates efficient search space exploration and Pareto front generation. A real-world case study demonstrates the model’s capability to generate diverse and high-quality non-dominated solutions. Correlation analysis among time, cost, and quality components validates the model’s logical consistency. Sensitivity analysis reveals the robustness of results under varying parameters. The weighted sum method (WSM) is employed to identify an optimal solution from the Pareto front. Comparative evaluation against existing algorithms (MOPSO, MOACO, LHS-NSGA III) confirms superior performance in convergence, diversity, and hypervolume. The proposed model provides project managers with a comprehensive and intelligent decision-support framework, ensuring optimized trade-offs and quality-integrated scheduling in complex construction environments.