Scholarlink

<p>Managing time, cost, quality, and safety (TCQS) effectively has become a critical challenge in contemporary construction projects, where increasing system complexity calls for robust multi-objective optimization methods. This research presents an Opposition-Enhanced Rao-3 metaheuristic, incorporating a simple opposition-based learning (OBL) strategy to reinforce the exploration strength of the conventional Rao-3 algorithm. By introducing opposition mechanisms during both the initial population generation and the iterative update stages, the proposed enhancement widens the search region, helps prevent premature convergence, and promotes greater diversity among candidate solutions. To assess its effectiveness, a practical 18-activity construction project with multiple execution alternatives is modeled. The case study reflects discrete time–cost trade-offs, different quality performance levels, and safety risk evaluations, resulting in a complex nonlinear multi-objective optimization framework representative of real planning conditions. The proposed method is compared with the standard Rao-3 algorithm and NSGA-III algorithm. Comparative results reveal that the Opposition-Enhanced Rao-3 achieves a more compact and evenly distributed Pareto front, while delivering notable improvements in project duration, total cost, cumulative quality performance, and overall safety risk. Relative to NDS-Rao-3 and NSGA-III, the enhanced approach demonstrates superior convergence accuracy, stronger solution stability, and greater robustness over multiple independent runs. In summary, the Opposition-Enhanced Rao-3 metaheuristic proves to be an efficient, dependable, and high-performing optimization tool for integrated TCQS decision-making, offering project planners a diverse set of balanced solutions that support safer, more cost-efficient, and higher-quality construction outcomes.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Improved Rao-3 algorithm using opposition learning for integrated project time–cost–quality–safety optimization

  • 期刊论文

  • 发表日期：

    2026年3月17日

查看全文
  • Gurpreet Kour Khalsa,
  • Akanksha Kulkarni,
  • Neha Verma,
  • Aditi Srivastava,
  • Sudhanshu Maurya,
  • Abdul Habib Zaray

摘要

Managing time, cost, quality, and safety (TCQS) effectively has become a critical challenge in contemporary construction projects, where increasing system complexity calls for robust multi-objective optimization methods. This research presents an Opposition-Enhanced Rao-3 metaheuristic, incorporating a simple opposition-based learning (OBL) strategy to reinforce the exploration strength of the conventional Rao-3 algorithm. By introducing opposition mechanisms during both the initial population generation and the iterative update stages, the proposed enhancement widens the search region, helps prevent premature convergence, and promotes greater diversity among candidate solutions. To assess its effectiveness, a practical 18-activity construction project with multiple execution alternatives is modeled. The case study reflects discrete time–cost trade-offs, different quality performance levels, and safety risk evaluations, resulting in a complex nonlinear multi-objective optimization framework representative of real planning conditions. The proposed method is compared with the standard Rao-3 algorithm and NSGA-III algorithm. Comparative results reveal that the Opposition-Enhanced Rao-3 achieves a more compact and evenly distributed Pareto front, while delivering notable improvements in project duration, total cost, cumulative quality performance, and overall safety risk. Relative to NDS-Rao-3 and NSGA-III, the enhanced approach demonstrates superior convergence accuracy, stronger solution stability, and greater robustness over multiple independent runs. In summary, the Opposition-Enhanced Rao-3 metaheuristic proves to be an efficient, dependable, and high-performing optimization tool for integrated TCQS decision-making, offering project planners a diverse set of balanced solutions that support safer, more cost-efficient, and higher-quality construction outcomes.