Scholarlink

<p>Construction project scheduling involves managing tradeoffs among multiple conflicting objectives, namely time, cost, quality, and safety (TCQS), under inherent uncertainty caused by variable site conditions, limited resource availability, and subjective assessments. This paper presents a robust hybrid optimization model that integrates fuzzy logic with opposition based learning (OBL) and the Non Dominated Sorting Genetic Algorithm III (NSGA III) to address TCQS tradeoffs under uncertainty. Triangular fuzzy numbers (TFNs) are used to represent imprecise input parameters such as activity durations, direct costs, quality scores, and safety risks. These TFNs are defuzzified using the centroid method to enable numerical processing. The model is designed to minimize project duration, total cost, and safety risk while maximizing quality. A real world case study involving 21 activities, each with five execution modes, is used to evaluate the approach. The proposed algorithm, calibrated with optimal evolutionary parameters, generates 25 high quality Pareto optimal solutions. Comparative analysis against established algorithms including MOACO, MOTLBO, MODE, standard NSGA III, and Fuzzy MOPSO demonstrates the superiority of the proposed model in terms of solution quality, diversity, and computational efficiency. Additional validation using tradeoff analysis and correlation metrics confirms the model’s robustness and practical applicability. This approach provides decision makers with a flexible and resilient tool for strategic construction scheduling in uncertain environments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Integrated robust fuzzy evolutionary optimization for time–cost–quality–safety trade-offs in construction projects

  • 期刊论文

  • 发表日期：

    2025年10月2日

查看全文
  • Akhilesh Kumar,
  • Pankaj Kumar,
  • Sumit Suman,
  • Abhishek Kumar,
  • Aditya Kumar,
  • Mani Bhushan,
  • A. Ganapathi Rao,
  • B Bikram Narayan

摘要

Construction project scheduling involves managing tradeoffs among multiple conflicting objectives, namely time, cost, quality, and safety (TCQS), under inherent uncertainty caused by variable site conditions, limited resource availability, and subjective assessments. This paper presents a robust hybrid optimization model that integrates fuzzy logic with opposition based learning (OBL) and the Non Dominated Sorting Genetic Algorithm III (NSGA III) to address TCQS tradeoffs under uncertainty. Triangular fuzzy numbers (TFNs) are used to represent imprecise input parameters such as activity durations, direct costs, quality scores, and safety risks. These TFNs are defuzzified using the centroid method to enable numerical processing. The model is designed to minimize project duration, total cost, and safety risk while maximizing quality. A real world case study involving 21 activities, each with five execution modes, is used to evaluate the approach. The proposed algorithm, calibrated with optimal evolutionary parameters, generates 25 high quality Pareto optimal solutions. Comparative analysis against established algorithms including MOACO, MOTLBO, MODE, standard NSGA III, and Fuzzy MOPSO demonstrates the superiority of the proposed model in terms of solution quality, diversity, and computational efficiency. Additional validation using tradeoff analysis and correlation metrics confirms the model’s robustness and practical applicability. This approach provides decision makers with a flexible and resilient tool for strategic construction scheduling in uncertain environments.