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<p>Time–Cost Trade-off Problems (TCTPs) are central to construction project planning, requiring decision-makers to identify optimal execution strategies that balance project duration with financial expenditure. Due to their combinatorial and multi-objective nature, TCTPs often demand advanced optimization techniques capable of simultaneously ensuring solution quality, diversity, and computational efficiency. This study proposes a Modified Adaptive Weight Multi-objective Grey Wolf Optimizer (MAWA-GWO) to address these challenges in an 18-activity construction project characterized by multiple modes and nonlinear time–cost relationships. The MAWA-GWO introduces an adaptive weighting mechanism that dynamically tunes leader influence and search intensity, thereby strengthening the algorithm’s exploration and exploitation capabilities throughout the optimization process. The performance of the proposed approach is benchmarked against two widely used metaheuristic-based multi-objective optimization Methods: the Multi-Objective Genetic Algorithm (MOGA) and the Random Key Variant for Time–Cost Optimization (RKV-TCO). Experimental results show that MAWA-GWO consistently outperforms both MOGA and RKV-TCO in terms of Pareto-front convergence, diversity, and dominance ratio. The algorithm produces a more comprehensive set of efficient time–cost trade-off alternatives for the 18-activity project, providing clearer insights for construction managers when evaluating scheduling and budgeting decisions. Overall, the findings highlight the superior robustness and optimization capability of MAWA-GWO, establishing it as an effective tool for complex construction management applications involving multi-objective decision environments.</p>

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Modified adaptive weight multi-objective GWO algorithm for construction time-cost trade-off optimization problems

  • 期刊论文

  • 发表日期：

    2026年4月9日

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  • Dileep Kumar M,
  • Rathinakumar V,
  • Padma Nilesh Mishra,
  • Bikash Ranjan Bag,
  • T. C. Manjunath,
  • Nageswara Rao Lakkimsetty

摘要

Time–Cost Trade-off Problems (TCTPs) are central to construction project planning, requiring decision-makers to identify optimal execution strategies that balance project duration with financial expenditure. Due to their combinatorial and multi-objective nature, TCTPs often demand advanced optimization techniques capable of simultaneously ensuring solution quality, diversity, and computational efficiency. This study proposes a Modified Adaptive Weight Multi-objective Grey Wolf Optimizer (MAWA-GWO) to address these challenges in an 18-activity construction project characterized by multiple modes and nonlinear time–cost relationships. The MAWA-GWO introduces an adaptive weighting mechanism that dynamically tunes leader influence and search intensity, thereby strengthening the algorithm’s exploration and exploitation capabilities throughout the optimization process. The performance of the proposed approach is benchmarked against two widely used metaheuristic-based multi-objective optimization Methods: the Multi-Objective Genetic Algorithm (MOGA) and the Random Key Variant for Time–Cost Optimization (RKV-TCO). Experimental results show that MAWA-GWO consistently outperforms both MOGA and RKV-TCO in terms of Pareto-front convergence, diversity, and dominance ratio. The algorithm produces a more comprehensive set of efficient time–cost trade-off alternatives for the 18-activity project, providing clearer insights for construction managers when evaluating scheduling and budgeting decisions. Overall, the findings highlight the superior robustness and optimization capability of MAWA-GWO, establishing it as an effective tool for complex construction management applications involving multi-objective decision environments.