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<p>Highway construction projects involve complex decision-making processes that require balancing multiple conflicting objectives, such as minimizing project completion time (PCT) and cost (PCC), while maximizing project quality (PQI) and minimizing safety risk (SRI). Traditional optimization techniques often fail to efficiently handle these trade-offs. This study proposes a novel multi-objective optimization framework based on the opposition-based learning multi-objective teaching–learning-based optimization (OBL-MOTLBO) algorithm to achieve an optimal trade-off among time, cost, quality, and safety risk in highway construction projects. The problem is formulated with four objective functions and realistic constraints, considering the non-linear relationship between activity duration and quality index. A real-world highway construction case study is conducted to validate the proposed framework, where multiple Pareto-optimal solutions are generated. The results demonstrate the effectiveness of OBL-MOTLBO in providing a set of optimal solutions that balance the competing project objectives. A trade-off analysis is performed, and a weighted sum method (WSM) is applied for decision-making. Comparative analysis with NSGA-III, MOACO, MOTLBO, and MOPSO highlights the superiority of OBL-MOTLBO in terms of convergence speed and solution quality. The proposed framework provides a robust decision-support tool for project managers and stakeholders in the construction industry to optimize resource allocation and improve project performance.</p>

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A novel multi-objective optimization framework for highway construction: time–cost-quality-safety risk trade-off using OBL-MOTLBO

  • 期刊论文

  • 发表日期：

    2025年3月21日

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  • Ajai Kumar Srivastava,
  • Mayank Chauhan,
  • Abhishek Rana

摘要

Highway construction projects involve complex decision-making processes that require balancing multiple conflicting objectives, such as minimizing project completion time (PCT) and cost (PCC), while maximizing project quality (PQI) and minimizing safety risk (SRI). Traditional optimization techniques often fail to efficiently handle these trade-offs. This study proposes a novel multi-objective optimization framework based on the opposition-based learning multi-objective teaching–learning-based optimization (OBL-MOTLBO) algorithm to achieve an optimal trade-off among time, cost, quality, and safety risk in highway construction projects. The problem is formulated with four objective functions and realistic constraints, considering the non-linear relationship between activity duration and quality index. A real-world highway construction case study is conducted to validate the proposed framework, where multiple Pareto-optimal solutions are generated. The results demonstrate the effectiveness of OBL-MOTLBO in providing a set of optimal solutions that balance the competing project objectives. A trade-off analysis is performed, and a weighted sum method (WSM) is applied for decision-making. Comparative analysis with NSGA-III, MOACO, MOTLBO, and MOPSO highlights the superiority of OBL-MOTLBO in terms of convergence speed and solution quality. The proposed framework provides a robust decision-support tool for project managers and stakeholders in the construction industry to optimize resource allocation and improve project performance.