Optimizing Enterprise Productivity in the Digital Economy: A Genetic Algorithm and Multi-Objective Approach

In the rapidly evolving digital economy, precise and efficient measurement of enterprise productivity is crucial for maintaining competitive advantage. This study introduces an innovative model for measuring enterprise productivity, leveraging the synergistic potential of multi-objective optimization and genetic algorithms. Our approach holistically analyzes various productivity factors, formulating a productivity factor model geared towards multi-objective tasks. We propose a quantitative method for characterizing enterprise productivity factors using multi-objective optimization (MOQMOO), which discerns key factors among the multitude. Subsequently, we introduce a novel productivity measurement model based on genetic algorithms, allowing for real-time monitoring and optimization of enterprise productivity. Our experimental results underscore the efficacy of the MOQMOO, achieving an HV value of 0.9578, thereby confirming the model’s significance in factor analysis. The proposed productivity measurement model also attains a mean average precision (mAP) value of 0.836, offering a pragmatic reference for strategic enterprise planning in the digital economy. This research contributes significantly to technology management and innovation in the knowledge economy, providing a robust framework for enterprises to navigate and thrive in the digital age.