Wang Dawei, Gao Bo, Zhang Lei
Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China.
CCCC Second Highway Consultants Company Limited, Wuhan, China.
PLoS One. 2025 May 21;20(5):e0320753. doi: 10.1371/journal.pone.0320753. eCollection 2025.
In this study, the optimization of construction machinery scheduling within roadbed construction projects is explored, taking into account both personnel fatigue and sequence-dependent setup times. A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. This algorithm reduces the number of iterations required for optimization and, subsequently, cuts down on energy consumption. Through rigorous analysis and comparison with existing algorithms, the proposed IHWGWO demonstrates a significant reduction in both iteration count and financial expenditure. Simulation outcomes confirm the accuracy and practicality of the model and algorithm, establishing a promising new approach for scheduling in construction engineering.
在本研究中，探讨了考虑人员疲劳和与顺序相关的设置时间的路基建设项目中施工机械调度的优化问题。开发了一个复杂的优化模型来模拟机械的最佳运行，旨在在解决工人疲劳带来的挑战的同时，最大限度地提高设备利用效率。引入了一种创新算法，即融合惩罚函数的改进混合灰狼和鲸鱼算法用于施工机械优化（IHWGWO），该算法纳入惩罚函数以有效处理约束条件。该算法减少了优化所需的迭代次数，进而降低了能源消耗。通过与现有算法进行严格分析和比较，所提出的IHWGWO在迭代次数和财务支出方面均显著减少。仿真结果证实了该模型和算法的准确性和实用性，为建筑工程调度建立了一种有前景的新方法。
