Distribution network under Omni-Channel integration contains many levels. There are one or more dealers at each level which forms a many-to-many distribution network. Consumers purchase a wide variety of products and their demands are uncertain, which constitutes a complex demand network and increases the complexity of the supply chain network. This paper focuses on the integrated optimization of supply chain distribution network and demand network and constructs the joint randomization planning model of location and routing. The goal is to minimize the total costs of the supply chain network under uncertain customer demands. Based on the traditional particle swarm optimization (PSO), this study introduces the collaborative idea to reduce the coding dimension, improves the boundary processing strategy, and adopts the mutation operator to expand the search space. A case study of distribution under Omni-Channel integration in a large enterprise was done. The validity of the model and the effectiveness of the proposed method were verified by numerical experiments.
Clustering has many applications in data mining and machine learning. Fuzzy clustering methods have been widely used in clustering. However, fuzzy clustering methods still have a fatal problem: the cluster radius sensitivity problem. The cluster radius sensitivity problem means that clusters with smaller radius will predominate in clustering and obtain more data points. Aiming at this problem, we propose a fuzzy separation and shrinkage clustering algorithm (FSC). FSC uses cluster membership degrees and cluster sizes to construct a new membership distribution, and then moves the data points according to this new membership distribution. The accuracies of our algorithm on wine, iris, balance scale and seeds are as follows: 98.82%, 97.27%, 63.07% and 91.34%. Our contributions are: (1) We propose a fuzzy separation and shrinkage clustering algorithm, which can solve the cluster radius sensitivity problem. (2) The performance of our algorithm on the UCI datasets goes beyond the benchmark algorithms.
Alfalfa is known as “the king of forage”. It not only has high yield, but also has good quality. Studying the development process of alfalfa will help scholars in this field grasp the future research direction. In this paper, the data of alfalfa field from 2009 to August 14, 2020 retrieved on Web of Science is taken as the research object. Firstly, this paper analyses the current research status by using basic statistical methods. The selected analysis dimensions include time, country, funding source etc. Then, we conduct topic clustering research based on Latent Dirichlet Allocation (LDA) model and the scientometrics method to compare and analyze its interests from both the overall and time perspectives. Finally, the experimental results are presented in diagrams. The results reveal that the development of alfalfa field tends to be stable. China and the United States have made remarkable achievements in the field of alfalfa. The topics integration and decomposition trend is weak, and the research content has strong coherence.
The global pandemic of COVID-19 has brought huge public health challenges to the world. To meet the challenge, researchers worldwide have carried out a series of clinical studies. This article aims to analyze the progress of COVID-19, and explore the development and main research directions in 2020. The clinical trials focus on the design of the trial plan, which can be registered on the platform after the design is completed. The purpose of clinical publications is to publish trial results, focusing on in vitro tests, drug screening and so on. Based on these characteristics, this paper analyzes both clinical publications and clinical trials, and explores the development of global clinical research in 2020 from countries, intervention methods and trial designs. The experimental results show that the United States and China have published the most publications and carried out the most clinical trials. The maximum intervention methods in clinical trials & publications are focused on the drugs.
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