In the context of global economic integration, the scale of projects in the architectural engineering industry continues to expand, and the complexity increases significantly. In this context, the accuracy and efficiency of resource scheduling have become critical factors in determining the competitiveness of enterprises. Traditional scheduling methods often rely on manual experience and simple mathematical models, and it is not easy to adapt to the rapidly changing market environment and refined management needs. This paper proposes an architectural engineering resource scheduling optimization model based on Python and a genetic algorithm, which aims to solve existing problems and realize the processing of high-dimensional and multi-objective optimization problems. The study uses a Python scientific computing library, and the platform is built based on a genetic algorithm. A comprehensive resource model is established, including personnel, equipment, materials, and other elements. Then, using the genetic algorithm’s global search ability and adaptive adjustment mechanism, a series of feasible solution sets are generated for the objective function of specific engineering projects with minimum total cost or minimum chemical engineering period, and the optimal solution is selected from them. In order to verify the actual effect of the model, a large-scale construction project is selected as a case for the simulation test. The results show that compared with the conventional workforce scheduling plan, the optimized resource allocation scheme reduces the overall cost by about 15% and the construction period by nearly 20%. More reasonable resource allocation reduces idleness and waste. The data proves that the resource scheduling strategy based on Python and the genetic algorithm has high feasibility and practicability and is of great value in promoting the digital transformation of the architectural engineering industry.
H. Wang (Fri,) studied this question.