Scholarlink

<p>Balancing time, cost, and quality objectives is a fundamental yet challenging task in construction project scheduling. This study employs an Advanced Jaya (A-Jaya) algorithm to solve the Time-Cost-Quality Trade-off Problem (TCQTP) for a construction project consisting of seven interdependent activities. The A-Jaya algorithm extends the conventional Jaya approach by integrating a crowded-distance mechanism to maintain population diversity and enhance convergence toward the true Pareto front. Each project activity is defined by multiple execution modes, representing various combinations of duration, cost, and quality levels. A multi-objective optimization framework is developed to obtain a set of Pareto-optimal solutions, offering decision-makers a range of efficient trade-offs. Algorithm performance is evaluated using standard multi-objective metrics such as Hypervolume (HV) and Spread (SP). The results demonstrate that A-Jaya achieves superior convergence and diversity with an HV value of 0.923 and SP value of 0.139, These results highlight the effectiveness of the crowded-distance-based Advanced Jaya algorithm in generating well-distributed Pareto-optimal solutions. Overall, A-Jaya proves to be a reliable and efficient optimization tool for managing time-cost-quality trade-offs in construction project scheduling.</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

Optimization of time-cost-quality trade-off problems using advanced Jaya algorithm in construction project scheduling

  • 期刊论文

  • 发表日期：

    2026年2月12日

查看全文
  • Sakambari Mishra,
  • Sudhanshu Maurya,
  • Lokesh Varshney,
  • Firdous Sadaf Mohammad Ismail,
  • Alamma BH,
  • T. C. Manjunath

摘要

Balancing time, cost, and quality objectives is a fundamental yet challenging task in construction project scheduling. This study employs an Advanced Jaya (A-Jaya) algorithm to solve the Time-Cost-Quality Trade-off Problem (TCQTP) for a construction project consisting of seven interdependent activities. The A-Jaya algorithm extends the conventional Jaya approach by integrating a crowded-distance mechanism to maintain population diversity and enhance convergence toward the true Pareto front. Each project activity is defined by multiple execution modes, representing various combinations of duration, cost, and quality levels. A multi-objective optimization framework is developed to obtain a set of Pareto-optimal solutions, offering decision-makers a range of efficient trade-offs. Algorithm performance is evaluated using standard multi-objective metrics such as Hypervolume (HV) and Spread (SP). The results demonstrate that A-Jaya achieves superior convergence and diversity with an HV value of 0.923 and SP value of 0.139, These results highlight the effectiveness of the crowded-distance-based Advanced Jaya algorithm in generating well-distributed Pareto-optimal solutions. Overall, A-Jaya proves to be a reliable and efficient optimization tool for managing time-cost-quality trade-offs in construction project scheduling.