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Aiming at the problems of the integration of ecotype supply chain platform, such as the uneven ability of suppliers, and the increasingly diversified market demand makes the platform cost difficult to reduce, etc., by positioning customer order decoupling point (CODP), considering the characteristics of ecotype supply chain with multiple orders, logistics services and manufacturing and production, a double objective and multiple decoupling point is constructed, which aims at the lowest total cost and the highest customer value positioning model, balancing cost and personalized demand, providing better customized rate decision-making scheme, and using the data provided by platform A, improving genetic algorithm, to verify the model solution. The customization rate and order quantity of platform operators under the decision-making of customer value preference and cost preference are solved. It is proved that the multi decoupling point positioning model can provide decision-making scheme for platform operators with different preferences, reduce the cost of ecological supply chain and improve customer satisfaction.

Recovering a decentralized low-rank matrix from an incomplete set of its entries is one of great research interests. Privacy makes our issue difficult. In this paper, we propose a novel scheme that allows analysts to perform great aggregate analysis while guaranteeing meaningful protection of each individuals privacy. Differential privacy aims to ensure means to maximize the accuracy of queries from statistical databases while minimizing the probabilities of identifying its records. With adding Gaussian noise, we are able to achieve this goal. First, we present an algorithm for private matrix completion. Secondly, we provide theoretical results for required Gaussian noise. Finally, we compare the performance of the proposed algorithm with the state-of-the-art, while both achieves the same level of differential privacy.