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<p>Time–cost–environmental impact trade-off problems (TCEITPs) are essential in sustainable construction project management, as decision-making must simultaneously address project duration, cost efficiency, and environmental performance. This study proposes a multi-objective optimization framework for solving TCEITPs using an opposition-based Rao-1 algorithm, in which opposition-based learning is incorporated to enhance population diversity and accelerate convergence toward well-distributed Pareto-optimal solutions. The effectiveness of the proposed approach is evaluated using a benchmark construction project comprising 25 activities with multiple execution alternatives. The obtained Pareto fronts are compared with those generated by the opposition-based multiple objective differential evolution (OMODE) method applied to the same case. Comparative results demonstrate that the proposed opposition-based Rao-1 algorithm is superior in terms of hypervolume (HV), spacing (SP), and the number of function evaluations required to reach high-quality Pareto solutions, indicating improved convergence efficiency and solution diversity. Overall, the findings confirm that the proposed framework provides a robust, efficient, and effective tool for solving complex TCEITPs, supporting informed decision-making in sustainable construction planning and contributing to the advancement of intelligent optimization techniques in project management.</p>

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Multi-objective time–cost–environmental impact trade-off optimization using an opposition-based Rao-1 algorithm

  • 期刊论文

  • 发表日期：

    2026年5月4日

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  • Pokkuluri Kiran Sree,
  • Miguel Villagómez Galindo,
  • Ana Beatriz Martínez Valencia,
  • Nageswara Rao Lakkimsetty,
  • P. Lakshmi Prasanna,
  • T. C. Manjunath

摘要

Time–cost–environmental impact trade-off problems (TCEITPs) are essential in sustainable construction project management, as decision-making must simultaneously address project duration, cost efficiency, and environmental performance. This study proposes a multi-objective optimization framework for solving TCEITPs using an opposition-based Rao-1 algorithm, in which opposition-based learning is incorporated to enhance population diversity and accelerate convergence toward well-distributed Pareto-optimal solutions. The effectiveness of the proposed approach is evaluated using a benchmark construction project comprising 25 activities with multiple execution alternatives. The obtained Pareto fronts are compared with those generated by the opposition-based multiple objective differential evolution (OMODE) method applied to the same case. Comparative results demonstrate that the proposed opposition-based Rao-1 algorithm is superior in terms of hypervolume (HV), spacing (SP), and the number of function evaluations required to reach high-quality Pareto solutions, indicating improved convergence efficiency and solution diversity. Overall, the findings confirm that the proposed framework provides a robust, efficient, and effective tool for solving complex TCEITPs, supporting informed decision-making in sustainable construction planning and contributing to the advancement of intelligent optimization techniques in project management.