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The interaction of a hybrid transshipment policy and customer switching behaviour will exacerbate the complexity of the structure of a hybrid transshipment policy. To cope with this problem, a discrete-time dynamic programming model framework with customer switching behaviour is developed. Based on this framework, we demonstrate that the retailer can obtain more profits with a hybrid transshipment than without one. Next, the existence of a reactive and preventive transshipment policy is shown, respectively. We further analyse the structural property of the holdback policy of reactive transshipment and give the threshold of customer switching rate when always rejecting the request. Meanwhile, a dominant preventive transshipment policy is formulated by which the retailer can control the inventory regardless of the influence of the preventive transshipment policy of the other as long as the inventory is observed by developing an easy-to-implement optimal hybrid transshipment strategy. In addition, the existence of an ordering Nash equilibrium of two retailers is proven. Then, we also illustrate the existence of a transshipment area and analyse the impact of the transshipment cost and switching rate on ordering, the hybrid transshipment policy, and profit by using numerical examples. Finally, we find that the retailer is more willing to adjust inventory by ordering when there is a lower transshipment price and adjust inventory by hybrid transshipment when there is a higher transshipment price.

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Scientific papers are an important form for researchers to summarize and display their research results. Information mining and analysis of scientific papers can help to form a comprehensive understanding of the subject. Aiming at the ignorance of contextual semantic information in current topic mining and the uncertainty of screening rules in association evolution research, this paper proposes a topic mining evolution model based on the BERT-LDA model. First, the model combines the contextual semantic information learned by the BERT model with the word vectors of the LDA model to mine deep semantic topics. Then construct topic filtering rules to eliminate invalid associations between topics. Finally, the relationship between themes is analyzed through the theme evolution, and the complex relationship between the themes such as fusion, diffusion, emergence, and disappearance is displayed. The experimental results show that, compared with the traditional LDA model, the topic mining evolution model based on BERTLDA can accurately mine topics with deep semantics and effectively analyze the development trend of scientific and technological paper topics.