关键词: 云平台, 数据驱动反向设计, 供应商资源, 主从协同优化, 双层嵌套遗传算法

Abstract: There were some problems in the traditional research of product and supplier resource collaboration, such as demand response lag and distortion caused by historical data or experience input and based on “normalization” or “hierarchical optimization”. Based on the analysis of the forward and backward coupling interaction characteristics of the two sides and the analysis of the data transmission path, a decision-making method of the leader-follower joint optimization system for products and supplier resources was proposed based on the DID of the cloud platform. Firstly, the collaborative configuration framework of products and supplier resources was constructed based on cloud platform DID. Secondly, the leader-follower joint optimization decision model was established based on cloud platform DID. Among them, the product configuration based on the positive customer demands acted as the leader, and supplier joint configuration acted as the follower to influence the decision-making of product configuration. The model trade-off solution was optimized by using bi-level nested genetic algorithm. Finally, a case of collaborative optimization between products and supplier resources of automobile seats was given. The results show that the proposed method has significant advantages in real representation of transfer and collaborative relationship of product and supplier joint configuration process.

Key words: cloud platform, data-informed inverse design(DID), supplier resource, leader-follower joint optimization, bi-level nested genetic algorithm