Periodic replenishment inventory models are widely used in practice, especially for inventory systems in which many different goods are purchased from the same supplier. However, most of periodic replenishment invento...
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ISBN:
(纸本)9781479937097
Periodic replenishment inventory models are widely used in practice, especially for inventory systems in which many different goods are purchased from the same supplier. However, most of periodic replenishment inventory models have assumed a fixed length of the replenishment periods. In practice, it is possible that the replenishment periods are of a stochastic length. This paper presents an inventory control model for deteriorating items in the case of random replenishment intervals and stock-dependent selling rate. The replenishment interval is assumed to obey from two different distributions, namely, exponential and uniform distributions. Also, shortages are allowed in the term of partial backordering. For this model, we provide the necessary and sufficient conditions of the existence and uniqueness of the optimal solutions and a procedure is also developed to determine the optimal solution for the proposed models. At last, numerical example is shown to illuminate the presented model.
This paper analyses the application status of cloud services to identify four factors that affect the security of enterprise cloud services (ECSs), including platform facilities, operational safety, operations managem...
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This paper analyses the application status of cloud services to identify four factors that affect the security of enterprise cloud services (ECSs), including platform facilities, operational safety, operations management, and legal factors. Based on the four factors, the grey fuzzy analytic hierarchy process (GFAHP) is used to construct an evaluation model for the security of ECSs. An example is investigated to demonstrate the proposed model.
This study addresses a two-stage supply chain scheduling problem, where the jobs need to be processed on the manufacturer's serial batching machine and then transported by vehicles to the customer for further proc...
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This paper proposes a method which uses RDF to express resources, tags and users of Folksonomy and allows users to assign tags freely by achieving the storage and query of RDF *** provides a means for completely open ...
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This paper proposes a method which uses RDF to express resources, tags and users of Folksonomy and allows users to assign tags freely by achieving the storage and query of RDF *** provides a means for completely open and sharing of information between *** it is also applied in the manufacture enterprises' website that it widens the application range of *** method can not only improve the degree of users' participation, but also realize the efficient management of mass data and the quick query of Information.
New product development (NPD) projects have been major forces driving company competitive advantage. All of them should align with corporate strategy so that the limited resources may be allocated effectively. However...
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ISBN:
(纸本)9781467355339
New product development (NPD) projects have been major forces driving company competitive advantage. All of them should align with corporate strategy so that the limited resources may be allocated effectively. However, few studies have been conducted to measure the extent that the NPD project matches with strategy. The paper identifies the cause-effect relationships between the strategy BSC measures for NPD projects based on strategy map and presents a BSC-ANP model to evaluate the strategic fit of projects. The causal relationships and interdependencies between projects and criteria are integrated with ANP model to produce the final priorities of projects with respect to their strategic fit. The proposed model was tested against empirical data drawn from NPD projects in an automobile company.
With the rapid development of the information technology, it is challenging for the traditional machine learning and data mining algorithms to deal with large scale explosive growth data. Manifold learning is a dimens...
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With the rapid development of the information technology, it is challenging for the traditional machine learning and data mining algorithms to deal with large scale explosive growth data. Manifold learning is a dimensionality reduction algorithm which can overcome some shortages of traditional linear dimensionality reduction methods. However, it is not useful for large scale data because of high complexity. In order to deal with the dimensionality reduction of large scale data, a distributed manifold learning algorithm is proposed based on MapReduce. Local sensitive hash functions are used to map the similarity points to the same bucket, then the geodesic distance between points in the same bucket can be computed by Euclidean norm according to the local homeomorphisms of Euclidean spaces of manifold and the geodesic distance among points between buckets can be computed by the modified geodesic distance formula which takes use of central points and edge points. Experiments on large scale of manmade dataset and real dataset show that this distributed manifold learning algorithm can approximate the geodesic distance between points effectively and it is useful for large scale dimensionality reduction.
Knowledge management has become one of the core competitions in the enterprise. In order to minimize product development cycle and promote market competitiveness, we need to optimize the level of knowledge management ...
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As analyzing and predicting the polarity of the sentiment plays an important role in understanding social phenomena and general society trends, sentiment classification problem has become a popular topic in academia a...
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As analyzing and predicting the polarity of the sentiment plays an important role in understanding social phenomena and general society trends, sentiment classification problem has become a popular topic in academia and industry in recent years. However, comparing with Bagging and Boosting, another popular ensemble method, i.e., Random Subspace, is paid much less attention to the sentiment classification problem. In this research, we propose a new ensemble method, RS-LSSVM, for sentiment classification based on Random Subspace and LSSVM. Ten public sentiment classification datasets are used to verify the effectiveness of the proposed RS-LSSVM. Experimental results reveal that RS-LSSVM can get the better results than the four base learners, Bagging, and Boosting. All these results indicate that RS-LSSVM can be used as an alternative method for sentiment classification.
The Internet accelerates the communication and understandings between people, which make information unprecedented important. Furthermore, it changes the way that people book rooms, which makes rooms-booking diversifi...
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The prediction of financial distress for financial institutions has been extensively researched for a long time. Latest studies have shown that such ensemble techniques have performed better than single AI technique i...
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