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<p>Managing time–cost–risk trade-offs is a crucial aspect of construction project management, as achieving an optimal balance among project duration, cost, and risk is essential for successful project delivery. This study introduces a novel multi-objective Rao-2 algorithm enhanced with opposition-based learning (ORao-2) to effectively optimize time–cost–risk trade-offs in construction projects. The integration of the opposition-based learning strategy enhances the algorithm’s exploration and exploitation capabilities, enabling faster convergence toward Pareto-optimal solutions. The proposed ORao-2 algorithm is validated through a 25-activity construction project case study, with performance evaluated using Hypervolume (HV), Spread, and correlation analysis to assess the quality and diversity of solutions. Comparative results demonstrate that the ORao-2 algorithm achieves competitive and well-balanced outcomes compared with other established approaches such as the Opposition-Based Non-Dominated Sorting Genetic Algorithm III (OBNSGAIII) algorithm. The findings highlight the potential of incorporating opposition-based learning into metaheuristic optimization techniques for complex project management challenges, offering both theoretical and practical contributions to the field.</p>

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An oppositional Rao-2 algorithm for multi-objective time–cost–risk trade-off optimization in construction scheduling

  • 期刊论文

  • 发表日期：

    2025年11月11日

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  • Girish Prasad Rath,
  • Víctor Daniel Jiménez Macedo,
  • Sudhanshu Maurya,
  • Sabhilesh Singh,
  • Amit Dhawan,
  • Krushna Chandra Sethi

摘要

Managing time–cost–risk trade-offs is a crucial aspect of construction project management, as achieving an optimal balance among project duration, cost, and risk is essential for successful project delivery. This study introduces a novel multi-objective Rao-2 algorithm enhanced with opposition-based learning (ORao-2) to effectively optimize time–cost–risk trade-offs in construction projects. The integration of the opposition-based learning strategy enhances the algorithm’s exploration and exploitation capabilities, enabling faster convergence toward Pareto-optimal solutions. The proposed ORao-2 algorithm is validated through a 25-activity construction project case study, with performance evaluated using Hypervolume (HV), Spread, and correlation analysis to assess the quality and diversity of solutions. Comparative results demonstrate that the ORao-2 algorithm achieves competitive and well-balanced outcomes compared with other established approaches such as the Opposition-Based Non-Dominated Sorting Genetic Algorithm III (OBNSGAIII) algorithm. The findings highlight the potential of incorporating opposition-based learning into metaheuristic optimization techniques for complex project management challenges, offering both theoretical and practical contributions to the field.