Optimization of resource allocation for power engineering construction scheduling based on genetic algorithms

A hybrid genetic algorithm framework is developed to solve the multi-objective resource allocation and scheduling problem in power engineering construction, with an emphasis on large-scale grid projects such as 500kV substations. The proposed approach incorporates dynamic crowding distance weighting and adaptive elite preservation to mitigate premature convergence and improve solution diversity in discrete, constraint-rich domains. Domain-specific mutation operators are used to explore the combinatorial allocation space. The experiments on a comprehensive construction dataset show that resource fluctuation and complex task dependencies exist in real life. The results show that the framework improves the distribution of Pareto front solutions by 18.7% and significantly reduces the allocation variance compared with the traditional evolutionary algorithm. The study further quantified the impact of dynamic resource buffering by 15%. This indicates that the scheduling and expansion robustness of the reachable solution set is improved. The method is integrated into the HVDC scheduling platform, which proves its feasibility in the commercial field and its adaptability to fluctuating project conditions. In the complex power engineering construction environment, these findings provide project managers with a technically reliable and scalable optimization tool that directly supports data-driven decision-making and risk mitigation.