Research on Intelligent Resource Planning Based on Improved Genetic Algorithm

This paper presents a robust intelligent resource planning framework powered by an enhanced genetic algorithm (GA), designed to navigate complex, dynamic environments. Traditional optimization methods often struggle with the unpredictability inherent in modern decision-making scenarios, including volatile market conditions, fluctuating operational costs, and changing output levels. These challenges hinder optimal resource allocation and strategic planning. To address these limitations, this study develops an adaptive GA model with advanced features, including adaptive mutation rates, elitism strategies, and local search mechanisms. The framework aims to optimize resource utilization by maximizing outcomes such as profitability or performance while respecting real-world constraints. It integrates data-driven insights, leveraging historical trends, predictive pricing models, and cost variations to ensure the resulting strategies are not only computationally efficient but also practical and economically sound. A case study utilizing real-world data from northern China validates the effectiveness of the approach, showcasing how the enhanced GA model outperforms conventional algorithms in both solution quality and processing speed. The results highlight the potential of intelligent optimization techniques to enhance decisionmaking and strategic planning across various industries. By providing actionable insights and efficient solutions, the framework empowers users to make informed decisions in uncertain environments, driving sustainable growth, operational stability, and profitability.