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<p>Material waste from suboptimal cutting practices in Mechanical, Electrical, and Plumbing (MEP) systems presents challenges in building engineering projects. While Building Information Modeling (BIM) provides data foundations, effective utilization for cutting optimization remains unresolved. This study proposed an optimization methodology for large-scale one-dimensional cutting stock problems derived from BIM data. A method for MEP piping data collection and statistical analysis was established using Revit API. A mathematical model minimizing raw material consumption costs was constructed, considering pipe requirements and material supply constraints. A column generation algorithm applicable to single and multi-specification stock pipe cutting was developed to obtain optimal cutting schemes through iterative processes. Compared with Genetic Algorithms (GA) and Greedy Algorithms (GRA), the proposed method demonstrated superior performance. Under single-specification conditions, material waste rate reached 0.54% with 1040&#xa0;m consumption. Multi-specification optimization maintained waste rates below 1% with 1025&#xa0;m consumption, confirming the approach’s feasibility and providing theoretical foundations for complex resource optimization problems.</p>

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Automated production cutting optimization for minimizing material waste of pipelines in prefabricated MEP systems based on integer programming

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月12日

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  • Xiongtao Fan,
  • Lu Yang,
  • Xuefeng Zhao

摘要

Material waste from suboptimal cutting practices in Mechanical, Electrical, and Plumbing (MEP) systems presents challenges in building engineering projects. While Building Information Modeling (BIM) provides data foundations, effective utilization for cutting optimization remains unresolved. This study proposed an optimization methodology for large-scale one-dimensional cutting stock problems derived from BIM data. A method for MEP piping data collection and statistical analysis was established using Revit API. A mathematical model minimizing raw material consumption costs was constructed, considering pipe requirements and material supply constraints. A column generation algorithm applicable to single and multi-specification stock pipe cutting was developed to obtain optimal cutting schemes through iterative processes. Compared with Genetic Algorithms (GA) and Greedy Algorithms (GRA), the proposed method demonstrated superior performance. Under single-specification conditions, material waste rate reached 0.54% with 1040 m consumption. Multi-specification optimization maintained waste rates below 1% with 1025 m consumption, confirming the approach’s feasibility and providing theoretical foundations for complex resource optimization problems.