Track guidance vehicle (RGV) is widely used in logistics warehousing and intelligent workshop, and its scheduling effectiveness will directly affect the production and operation efficiency of enterprises. In practical operation, central information system often lacks flexibility and timeliness. By contrast, mobile computing can balance the central information system and the distributed processing system, so that useful, accurate, and timely information can be provided to RGV. In order to optimize the RGV scheduling problem in uncertain environment, a genetic algorithm scheduling rule (GAM) using greedy algorithm as the genetic screening criterion is proposed in this paper. In the experiment, RGV scheduling of two-step processing in an intelligent workshop is selected as the research object. The experimental results show that the GAM model can carry out real-time dynamic programming, and the optimization efficiency is remarkable before a certain threshold.

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Purpose In recent years, personalized recommendations have facilitated easy access to users' personal information and historical interactions, thereby improving recommendation effectiveness. However, due to privacy risk concerns, it is essential to balance the accuracy of personalized recommendations with privacy protection. Accordingly, this paper aims to propose a neural graph collaborative filtering personalized recommendation framework based on federated transfer learning (FTL-NGCF), which achieves high-quality personalized recommendations with privacy protection. Design/methodology/approach FTL-NGCF uses a third-party server to coordinate local users to train the graph neural networks (GNN) model. Each user client integrates user–item interactions into the embedding and uploads the model parameters to a server. To prevent attacks during communication and thus promote privacy preservation, the authors introduce homomorphic encryption to ensure secure model aggregation between clients and the server. Findings Experiments on three real data sets (Gowalla, Yelp2018, Amazon-Book) show that FTL-NGCF improves the recommendation performance in terms of recall and NDCG, based on the increased consideration of privacy protection relative to original federated learning methods. Originality/value To the best of the authors’ knowledge, no previous research has considered federated transfer learning framework for GNN-based recommendation. It can be extended to other recommended applications while maintaining privacy protection.

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