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<p>Efficient construction scheduling is a critical challenge due to the competing objectives and real-world constraints, including limited resources, safety risks, and environmental concerns. This study proposes a novel multi-objective optimization framework for resource-constrained construction scheduling, simultaneously optimizing five key objectives: project completion time (PCT), project completion cost (PCC), project quality index (PQI), safety risk (PSR), and environmental impact (PEI). The framework utilizes an enhanced Opposition-Based Non-Dominated Sorting Genetic Algorithm III (OBNSGA-III), integrating opposition-based learning and reference-point selection to improve convergence and diversity. A real-world tunnel construction project involving 25 activities with discrete execution modes is used to validate the model. The OBNSGA-III algorithm generates 26 Pareto-optimal solutions, each offering distinct trade-offs among the five objectives. Sensitivity and correlation analyses confirm the strong interdependencies among the objectives, while benchmarking against existing algorithms demonstrates superior performance in terms of convergence, diversity, and robustness. The weighted sum method is used to identify the most balanced solution, offering a practical decision-support tool for construction project managers. This research presents an innovative, scalable approach to scheduling that balances efficiency, cost, quality, safety, and environmental sustainability in complex construction environments.</p>

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Integrated optimization of construction schedules: a five-objective approach for time, cost, quality, safety, and environmental sustainability using OBNSGA-III

  • 期刊论文

  • 发表日期：

    2025年9月5日

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  • Ashish Panthi,
  • Aslam Hussain

摘要

Efficient construction scheduling is a critical challenge due to the competing objectives and real-world constraints, including limited resources, safety risks, and environmental concerns. This study proposes a novel multi-objective optimization framework for resource-constrained construction scheduling, simultaneously optimizing five key objectives: project completion time (PCT), project completion cost (PCC), project quality index (PQI), safety risk (PSR), and environmental impact (PEI). The framework utilizes an enhanced Opposition-Based Non-Dominated Sorting Genetic Algorithm III (OBNSGA-III), integrating opposition-based learning and reference-point selection to improve convergence and diversity. A real-world tunnel construction project involving 25 activities with discrete execution modes is used to validate the model. The OBNSGA-III algorithm generates 26 Pareto-optimal solutions, each offering distinct trade-offs among the five objectives. Sensitivity and correlation analyses confirm the strong interdependencies among the objectives, while benchmarking against existing algorithms demonstrates superior performance in terms of convergence, diversity, and robustness. The weighted sum method is used to identify the most balanced solution, offering a practical decision-support tool for construction project managers. This research presents an innovative, scalable approach to scheduling that balances efficiency, cost, quality, safety, and environmental sustainability in complex construction environments.