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<p>Construction projects are inherently affected by uncertainties arising from site variability, resource availability, and subjective scheduling judgments. This study proposes a unified hybrid framework that integrates Fuzzy Logic, Opposition-Based Learning (OBL), and the Non-Dominated Sorting Genetic Algorithm III (NSGA-III) to address multi-objective trade-offs involving Time, Cost, Quality, and Safety (TCQS) under uncertainty. Triangular Fuzzy Numbers (TFNs) are used to model uncertain parameters such as activity durations, direct costs, quality indices, and safety risks. These are defuzzified using the centroid method to enable effective optimization. A real-world construction case study comprising 21 activities, each with five execution modes, is used to demonstrate the approach. The proposed Fuzzy-OBL-NSGA-III algorithm yields 25 high-quality Pareto-optimal solutions, balancing the conflicting TCQS objectives. Performance metrics and comparative analysis with existing models such as MOACO, MOTLBO, MODE, standard NSGA-III, and Fuzzy-MOPSO confirm the superior diversity, convergence, and computational efficiency of the proposed model. Trade-off plots and correlation analyses further validate its decision-support capabilities. The framework provides construction planners with a dynamic, scalable, and uncertainty-resilient scheduling tool, enabling strategic decision-making in complex, real-life project environments.</p>

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Unified fuzzy–opposition-based NSGA-III framework for multi-objective time–cost–quality–safety optimization in construction scheduling

  • 期刊论文

  • 发表日期：

    2026年4月23日

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  • Om Prakash Singh,
  • Niraj Kumar Singh,
  • Subhash Chandra Bhushan,
  • Madan Kumar,
  • Sandeep Kumar Sahoo,
  • Mani Bhushan,
  • Jagannath Padhy

摘要

Construction projects are inherently affected by uncertainties arising from site variability, resource availability, and subjective scheduling judgments. This study proposes a unified hybrid framework that integrates Fuzzy Logic, Opposition-Based Learning (OBL), and the Non-Dominated Sorting Genetic Algorithm III (NSGA-III) to address multi-objective trade-offs involving Time, Cost, Quality, and Safety (TCQS) under uncertainty. Triangular Fuzzy Numbers (TFNs) are used to model uncertain parameters such as activity durations, direct costs, quality indices, and safety risks. These are defuzzified using the centroid method to enable effective optimization. A real-world construction case study comprising 21 activities, each with five execution modes, is used to demonstrate the approach. The proposed Fuzzy-OBL-NSGA-III algorithm yields 25 high-quality Pareto-optimal solutions, balancing the conflicting TCQS objectives. Performance metrics and comparative analysis with existing models such as MOACO, MOTLBO, MODE, standard NSGA-III, and Fuzzy-MOPSO confirm the superior diversity, convergence, and computational efficiency of the proposed model. Trade-off plots and correlation analyses further validate its decision-support capabilities. The framework provides construction planners with a dynamic, scalable, and uncertainty-resilient scheduling tool, enabling strategic decision-making in complex, real-life project environments.