In the ever-evolving landscape of construction management, a groundbreaking study led by Wang Xudong from the School of Architectural and Engineering at Shanghai Zhongqiao Vocational and Technical University has introduced a novel approach to construction progress and cost management (CPCM). Published in the journal *Nonlinear Engineering*, the research presents an improved genetic algorithm (GA) that promises to revolutionize how construction projects are planned and executed.
The study addresses a critical need in the construction industry: the efficient management of progress and costs. Wang Xudong and his team have developed a method that not only optimizes these aspects but also does so with remarkable speed and stability. “Our method reaches the upper limit of cost and progress optimization much faster than existing algorithms,” Wang explains. This efficiency is a game-changer, particularly for large-scale projects where time and budget overruns can have significant financial implications.
The proposed GA introduces a serial operation strategy and defines a project valuation function, constructing a CPCM resource library. The tangent function is used to simulate key relationships in the construction process, compressing and optimizing both progress and cost. The results are impressive. On the construction engineering cost standards (CECS) dataset, the method reaches the upper limit in just 23 generations, 66.67% faster than the multi-stage genetic algorithm (MGA) and 51.06% faster than the quantum genetic algorithm (QGA). For the building engineering dataset (BED), it achieves the upper limit in 30 generations, 33.33% faster than MGA and 49.18% faster than QGA.
The stability of the proposed method is equally noteworthy. The mean squared error (MSE) fluctuates within a range of 0.04 after 22 and 23 generations for the CECS and BED datasets, respectively, outperforming both MGA and QGA. Additionally, the method demonstrates higher computational efficiency, with calculation time increasing by less than 30 seconds as the number of engineering steps increases from 5 to 25.
The commercial impact of this research is substantial. By generating plans that reduce construction costs by 230,000 yuan, the method provides a valuable tool for project managers and stakeholders. “This research method has good computational efficiency and can effectively generate references for project construction cost management plans,” Wang states. This efficiency and accuracy can lead to significant savings and improved project outcomes, benefiting not only the construction industry but also the broader energy sector, where large-scale projects are common.
The implications of this research extend beyond immediate cost savings. The improved GA method could shape future developments in construction management, offering a more reliable and efficient way to handle complex projects. As the construction industry continues to evolve, the integration of advanced algorithms like the one proposed by Wang Xudong and his team could become a standard practice, driving innovation and improving project outcomes.
In conclusion, the study published in *Nonlinear Engineering* (translated as *Nonlinear Engineering*) represents a significant advancement in construction progress and cost management. By leveraging the power of genetic algorithms, Wang Xudong and his team have developed a method that promises to optimize construction projects more efficiently and effectively than ever before. This research not only addresses current industry needs but also paves the way for future innovations, making it a crucial development for the construction and energy sectors.