Data-driven Industrial Enterprise Order Scheduling and Optimization

As China's industrial enterprises continue to grow and competition intensifies, efficient order scheduling and management have become essential to operational success. Many enterprises face challenges such as dispersed and unstandardized order data, reliance on manual scheduling, and limited data analysis capabilities. This study leverages priority-based and genetic algorithms, supported by extensive original fixed data (e.g., reservation order data, raw material supply data, production process data) and system-generated production data (e.g., sub-order data, raw material demand data), to design an optimized order scheduling method. The proposed algorithm not only meets order acceptance and scheduling requirements but also improves the rationality of order scheduling and the effectiveness of resource allocation. By reducing the rate of incomplete orders caused by raw material supply constraints and tight delivery times, this algorithm offers industrial enterprises a standardized production process, thereby enhancing overall operational efficiency and competitiveness.