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<p>Efficient project scheduling requires balancing conflicting objectives such as time and cost. This study proposes a Modified Adaptive Weight Multi-Objective Mountain Gazelle Optimizer (MAWA-MGO) to address the Construction Time–Cost Trade-off (TCT) problem. The algorithm enhances the original MGO by using an adaptive weight adjustment strategy that dynamically balances exploration and exploitation, preventing premature convergence and improving Pareto-optimal solution quality. A benchmark 9-activity project was used to test the model, minimizing both project duration and total cost under precedence and resource constraints. Comparative results with non-dominated sorting GA (NSGA-II), multi-objective particle swarm optimization (MOPSO), and standard MGO show that MAWA-MGO achieves a more diverse and convergent Pareto front, a significant reduction in duration and cost, respectively. The findings confirm MAWA-MGO’s robustness and practicality as a decision-support tool for optimizing construction schedules. Future studies may extend it to include quality and environmental objectives for sustainable planning.</p>

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Modified adaptive weight multi-objective mountain gazelle optimizer for construction time-cost trade-off optimization problems

  • 期刊论文

  • 发表日期：

    2025年12月5日

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  • Mukesh Pandey,
  • Yusuf Baltacı,
  • Sakambari Mishra,
  • Mahesh Sharma,
  • Subash Kumar Bhattarai,
  • Krushna Chandra Sethi

摘要

Efficient project scheduling requires balancing conflicting objectives such as time and cost. This study proposes a Modified Adaptive Weight Multi-Objective Mountain Gazelle Optimizer (MAWA-MGO) to address the Construction Time–Cost Trade-off (TCT) problem. The algorithm enhances the original MGO by using an adaptive weight adjustment strategy that dynamically balances exploration and exploitation, preventing premature convergence and improving Pareto-optimal solution quality. A benchmark 9-activity project was used to test the model, minimizing both project duration and total cost under precedence and resource constraints. Comparative results with non-dominated sorting GA (NSGA-II), multi-objective particle swarm optimization (MOPSO), and standard MGO show that MAWA-MGO achieves a more diverse and convergent Pareto front, a significant reduction in duration and cost, respectively. The findings confirm MAWA-MGO’s robustness and practicality as a decision-support tool for optimizing construction schedules. Future studies may extend it to include quality and environmental objectives for sustainable planning.