A collaborative resequencing approach enabled by multi-core PREA for a multi-stage automotive flow shop

The initial production sequences of a multi-stage automotive flow shop (MSAFS) have a chance to be disturbed by emergency orders from the market or warehouses. This kind of disturbance can hinder a timely resequencing, and an improper resequencing scheme would increase the total production costs and delivery delays. To tackle this problem, a collaborative resequencing approach for a MSAFS is proposed enabled by a multi-core promisingregion evolutionary algorithm (PREA) to deal with emergency orders for an efficient optimization of resequencing during the automotive production. First, considering the characteristics of automotive production, a sliding window-based collaborative resequencing strategy is proposed to cope with emergency orders. Then, a model that integrates the welding, painting, and assembly processes is described mathematically to minimize production costs and delivery delays. Next, a multi-core PREA is developed to optimize the collaborative resequencing model with high efficiency. Finally, a case study is conducted to verify the feasibility and superiority of the proposed approach. The numerical results indicate that the proposed approach is capable of realizing an effective resequencing optimization with high computational performance in terms of handling emergency orders for automotive production.