A product-driven system for incorporating rush orders into a job shop scheduling problem

In modern manufacturing, cyber manufacturing systems integrate physical elements with virtual product in production. These systems have specialized stations, thus generating a high probability of interruptions in the production line and requiring schedule adjustments. This study proposes an innovative production scheduling model named PDS-SBH, which incorporates intelligent products to mitigate the impact of disruptions in job shop environments. The results demonstrate a remarkable buffering capacity, with average reductions of 11.08%, 9.97%, and 3.42% when introducing urgent products at 10%, 50%, and 90% of the completion time, respectively. In 558 simulations with disturbances, the PDS-SBH achieves an 8.16% reduction in the makespan value, with only 6.45% of instances showing no buffering. The detailed analysis reveals that incorporating the product at 10% achieves the greatest buffering, utilizing the available time to reorganize post-disturbance tasks efficiently. In instances with a higher number of operations, the model demonstrates effectiveness in buffering deterioration in 98.33% of simulations. This approach provides flexibility to the sequencing process, adapting to changes in the production plan without significant human intervention. Computational evidence highlights the competitiveness of the model in disturbance-free instances and its sustained ability to buffer deterioration in the presence of disruptions, positioning it as a valuable tool for enhancing the performance of production systems affected by adverse events. Collectively, these results support the significant contribution and practical applicability of the PDS-SBH model in dynamic industrial environments.