New model to streamline customer order scheduling

The Customer Order Scheduling (COS) problem involves organizing a set of orders, each characterized by a release time, a due date, and a processing time, with the objective of minimizing the costs associated with delivery delays. This study introduces an innovative approach, the Streamline COS, which integrates order scheduling optimization with improved resource utilization and a reduction in production inefficiencies. We present a model aimed at minimizing an objective function that encompasses delay penalties and the optimization of resource usage. Additionally, we introduce an optimized COS algorithm, named SCOS (Streamline COS), designed to find the optimal solution. The efficacy of this approach is validated through experiments with several datasets of varying sizes and instances, demonstrating that the Streamline COS method significantly enhances resource management, reduces delays, and optimizes order scheduling, while offering flexible priority management. This approach provides a practical and effective solution to complex scheduling problems and opens new research avenues, particularly in integrating dynamic constraints and leveraging optimization and machine learning techniques for further performance improvements.