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<p>Considering duration, cost, and quality constraints, this study develops a multi-objective optimization model for project scheduling. The framework employs trapezoidal fuzzy numbers to characterize activity durations and a bell-shaped function to define the duration-quality trade-off. An optimal schedule is derived using a genetic algorithm. To mitigate uncertainties in construction progress, four duration risk categories—resource, operational, human, and environmental—are quantified through risk coefficients and weighting factors to establish a robust buffer sizing mechanism. Upon 50% completion of the planned workload, a grey GM(1,1) model is implemented to forecast activity finish times based on real-time progress monitoring. Furthermore, a dynamic buffer allocation model is established by integrating initial buffers, early completion gains, and cumulative buffer consumption. Application to a pumped storage power station project demonstrates that the proposed method reduces the total duration by 18 days, confirming its efficiency and practical utility in complex engineering environments.</p>

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Dynamic Allocation of Critical Chain Buffers for Multi-objective Optimization and Project Schedule Risk Management

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月18日

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  • Xueqing Zhang,
  • Hongzhe Qin,
  • Zhiqiang Fu,
  • Shiwei Ding,
  • Jikun Zhang,
  • Dongke Li,
  • Xiangtian Nie,
  • Tianyu Fan

摘要

Considering duration, cost, and quality constraints, this study develops a multi-objective optimization model for project scheduling. The framework employs trapezoidal fuzzy numbers to characterize activity durations and a bell-shaped function to define the duration-quality trade-off. An optimal schedule is derived using a genetic algorithm. To mitigate uncertainties in construction progress, four duration risk categories—resource, operational, human, and environmental—are quantified through risk coefficients and weighting factors to establish a robust buffer sizing mechanism. Upon 50% completion of the planned workload, a grey GM(1,1) model is implemented to forecast activity finish times based on real-time progress monitoring. Furthermore, a dynamic buffer allocation model is established by integrating initial buffers, early completion gains, and cumulative buffer consumption. Application to a pumped storage power station project demonstrates that the proposed method reduces the total duration by 18 days, confirming its efficiency and practical utility in complex engineering environments.