Source Journal of Chinese Scientific and Technical Papers
Included as T2 Level in the High-Quality Science and Technology Journals in the Field of Architectural Science
Core Journal of RCCSE
Included in the CAS Content Collection
Included in the JST China
Indexed in World Journal Clout Index (WJCI) Report
Volume 56 Issue 2
Feb.  2026
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Article Contents
CHEN Yueshi, MA Minglei, ZHANG Shiqian, ZHENG Zhigang, HOU Xubin, ZHANG Xinyu. Research on Production Line Balance Optimization for UHPC Light-Gauge Steel Framing Sandwich Insulation Wall Panels Based on Mixed-Integer Nonlinear Programming and Genetic Algorithm[J]. INDUSTRIAL CONSTRUCTION, 2026, 56(2): 127-135. doi: 10.3724/j.gyjzG25082104
Citation: CHEN Yueshi, MA Minglei, ZHANG Shiqian, ZHENG Zhigang, HOU Xubin, ZHANG Xinyu. Research on Production Line Balance Optimization for UHPC Light-Gauge Steel Framing Sandwich Insulation Wall Panels Based on Mixed-Integer Nonlinear Programming and Genetic Algorithm[J]. INDUSTRIAL CONSTRUCTION, 2026, 56(2): 127-135. doi: 10.3724/j.gyjzG25082104

Research on Production Line Balance Optimization for UHPC Light-Gauge Steel Framing Sandwich Insulation Wall Panels Based on Mixed-Integer Nonlinear Programming and Genetic Algorithm

doi: 10.3724/j.gyjzG25082104
  • Received Date: 2025-08-21
    Available Online: 2026-04-11
  • Publish Date: 2026-02-20
  • With the advancement of prefabricated buildings and green construction, Ultra-High Performance Concrete (UHPC) Light-Gauge Steel Framing (LGSF) sandwich insulation wall panels have emerged as a key focus in developing new building enclosures, owing to their superior mechanical properties, thermal insulation performance, and construction efficiency. However, the manufacturing of such panels involves complex processes, multiple stages, and significant variations in takt time, making traditional scheduling methods inadequate for meeting the demands of high-efficiency and energy-synergized production. To address this, focusing on a typical wall panel production line and targeting issues such as unbalanced operation takt and irrational resource allocation, this study developed a Mixed-Integer Nonlinear Programming (MINLP) model aimed at maximizing the line-balancing rate. Furthermore, an intelligent optimization method was designed by hybridizing a genetic algorithm with simulated annealing and dynamic mutation control, thereby solving the scheduling challenges arising from uneven operation durations and discrete resource allocation. Validation through field data and simulations demonstrated that the optimized solution significantly enhanced line takt balance and resource utilization, indicating good engineering adaptability and promotion value.
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