Collaborative Scheduling Optimization Method for Multi-Stage Automobile Engine Hybrid Flow Shop

The production of automobile engines primarily concerns three workshops: casting, machining and assembly. Any stagnation in any of these stages will have a detrimental impact on the operation of downstream production, resulting in increased costs and production delays. Hence, it is imperative to establish a rational scheduling system for the whole production. Therefore, this study proposes a collaborative scheduling optimization method for multi-level hybrid flow shop of automobile engine. Initially, the collaborative production relationships between the workshops are examined. Subsequently, an optimization model of collaborative scheduling for the multi-stage hybrid flow shop is formulated, focusing on reducing collaborative production costs and minimizing the maximum completion time. Furthermore, considering the discrete nature of the scheduling problem, the study explores encoding/decoding strategies based on random key techniques to bridge the gap between continuous algorithms and discrete problems. Consequently, a distributed reference vector guided evolutionary algorithm (DRVEA) is introduced to solve the model. Finally, the effectiveness and superiority of the proposed method are validated through a case study using practical data from an automobile engine enterprise.