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<p>Construction project scheduling involves complex trade-offs between time, cost, and quality (TCQ), often under conditions of uncertainty. This paper presents a novel approach using a fuzzy multi-objective particle swarm optimization (Fuzzy-MOPSO) algorithm to address the TCQ optimization problem in uncertain environments. By integrating fuzzy set theory with MOPSO, the model accommodates imprecise data and stakeholder preferences, allowing for a more realistic representation of construction project dynamics. Three objective functions are considered: minimizing project completion time (PCT), minimizing project construction cost (PCC), and maximizing project quality index (PQI). A case study involving a real-world construction project is employed to demonstrate the effectiveness of the proposed methodology. Twenty-six Pareto-optimal solutions were obtained and analyzed through trade-off plots and correlation analysis. The performance of the Fuzzy-MOPSO algorithm is benchmarked against other popular multi-objective optimization techniques, including NSGA-III, MODE, and MOTLBO. Results show that the proposed algorithm outperforms existing methods in convergence, diversity, and solution quality, achieving a more balanced TCQ optimization. The findings suggest that Fuzzy-MOPSO is a robust and efficient tool for construction managers seeking optimal schedules under uncertainty, contributing to better decision-making and resource allocation in complex project environments.</p>

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Uncertainty-aware optimization of construction time–cost-quality trade-offs via Fuzzy-MOPSO

  • 期刊论文

  • 发表日期：

    2025年8月7日

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  • Amir Prasad Behera,
  • Cleanton Sabar,
  • Aditya Kumar,
  • Akash Ranjan,
  • Rahul Kumar Ranjan,
  • Mani Bhushan,
  • Soumyaprakash Sahoo,
  • Bimalendu Dash

摘要

Construction project scheduling involves complex trade-offs between time, cost, and quality (TCQ), often under conditions of uncertainty. This paper presents a novel approach using a fuzzy multi-objective particle swarm optimization (Fuzzy-MOPSO) algorithm to address the TCQ optimization problem in uncertain environments. By integrating fuzzy set theory with MOPSO, the model accommodates imprecise data and stakeholder preferences, allowing for a more realistic representation of construction project dynamics. Three objective functions are considered: minimizing project completion time (PCT), minimizing project construction cost (PCC), and maximizing project quality index (PQI). A case study involving a real-world construction project is employed to demonstrate the effectiveness of the proposed methodology. Twenty-six Pareto-optimal solutions were obtained and analyzed through trade-off plots and correlation analysis. The performance of the Fuzzy-MOPSO algorithm is benchmarked against other popular multi-objective optimization techniques, including NSGA-III, MODE, and MOTLBO. Results show that the proposed algorithm outperforms existing methods in convergence, diversity, and solution quality, achieving a more balanced TCQ optimization. The findings suggest that Fuzzy-MOPSO is a robust and efficient tool for construction managers seeking optimal schedules under uncertainty, contributing to better decision-making and resource allocation in complex project environments.