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Approximation algorithms for two-stage supply chain scheduling of production and distribution

International Journal of Systems Science: Operations and Logistics 2015年第2期2卷 78-89页

作者： Cheng, Bayi Li, Kai Hu, Xiaoxuan School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China

In this paper, we investigate a class of two-stage optimisation problems for manufacturers in supply chains. The objective is to optimise production and outbound distribution. In the production part, the manufacturer has identical batch-processing machines with limited capacity in size. The processing times and sizes of jobs are determined by the customer. As long as the total size of jobs in a batch does not exceed the machine capacity, the batch can be processed on any machine. The processing of a batch cannot be interrupted until all the jobs in the batch have been completed. In the distribution part, the manufacturer assigns the completed jobs in groups and delivers them to the customer with its own vehicles. We propose the model of minimising service span, which lasts from the processing of the first job to the delivery of the last job. We investigate two types of machine configuration, including single machine and parallel machines. In the vehicle configuration, we consider two cases including infinite vehicles and a fixed number of vehicles. We show all the problems under investigation are NP-hard. For each problem, we design an effective polynomial time algorithm. We analyse the time complexity and show the performance guarantees of the algorithms. © 2014, © 2014 Taylor & Francis.

关键词： approximation algorithm arbitrary-size jobs batch-processing machines supply chain scheduling

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Corrigendum to ‘Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms’ [Information Sciences volumes 367–368 (2016) 80–104]

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Information Sciences 2017年 403-404卷 55-55页

作者： Jiaqi Zhao Vitor Basto Fernandes Licheng Jiao Iryna Yevseyeva Asep Maulana Rui Li Thomas Bäck Ke Tang Michael T.M. Emmerich Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education International Research Center for Intelligent Perception and Computation Joint International Research Laboratory of Intelligent Perception and Computation Xidian University Xi'an Shaanxi Province 710071 China School of Technology and Management Computer Science and Communications Research Centre Polytechnic Institute of Leiria 2411-901 Leiria Portugal Faculty of Technology De Montfort University Gateway House 5.33 The Gateway LE1 9BH Leicester UK Multicriteria Optimization Design and Analytics Group LIACS Leiden University Niels Bohrweg 1 2333-CA Leiden The Netherlands Microsoft Research Asia Beijing 100190 China Nature Inspired Computation and Applications Laboratory (NICAL) the USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI) School of Computer Science and Technology University of Science and Technology of China (USTC) Hefei Anhui 230027 China

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Collaborative optimization of profit and cost of hybrid cloud storage service

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International Journal of u- and e- Service, Science and Technology 2015年第2期8卷 185-196页

作者： Sun, Chao-Ping Zhao, Jing Ruan, Su-Mei School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China School of Business Anhui University of Finance and Economics Bengbu China

Aiming at hybrid cloud storage service (HCSS), this paper conducts research on collaborative optimization of corresponding profits and costs. Profit function of HCSS considers flexibility, extendibility, decrease in investment, improvement on access speed, data sharing, disaster recovery, and green IT, while cost function of HCSS considers data transmission, data storage, and facilities of hardware and software. Based on profit and cost functions of HCSS, a goal programming model is constructed to collaboratively optimize profits and costs of HCSS. The model is validated by making a comparative analysis in two representative contexts. © 2015 SERSC.

关键词： Profitability

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CO-RS: A new method for opinion mining based on semi-supervised learning and ensemble learning

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ICIC Express Letters 2015年第7期9卷 1803-1808页

作者： Wang, Gang Li, Ningning School of Management Hefei University of Technology No. 193 Tunxi Road Hefei China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei University of Technology No. 193 Tunxi Road Hefei China

In recent years many machine learning methods have been proposed for opinion mining and the effectiveness of applying them to opinion mining has also been approved theoretically. However, machine learning methods encounter many problems when they are used in practice. One of the problems is to construct learning systems with strong generalization ability. In this study, we propose, Co-RS, a semi-supervised method for opinion mining based on co-training and Random Subspace. Co-RS tries to get the strong generalization ability by integrating the strength of two machine learning paradigms, i.e., semi-supervised learning and ensemble learning. Moreover, four public opinion datasets were investigated to verify the effectiveness of proposed method. Empirical results reveal that Co-RS gets the best result among the compared methods. These results illustrate that Co-RS can be used as a viable method for opinion mining. © 2015, ISSN 1881-803X.

关键词： Sentiment analysis

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Disagreement-based semi-supervised learning method for enterprise credit risk assessment

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ICIC Express Letters 2015年第6期9卷 1671-1676页

The enterprise credit risk assessment problem has long been regarded as an important and widely studied issue in both academia and industry. However, unla-beled data problem is paid less attention to in the credit risk assessment community. To fill in this research gap, one of popular disagreement semi-supervised learning methods, co-training, is introduced to exploit the unlabeled data to improve the enterprise credit risk assessment performance. Co-training allows one to start with just a few labeled instances to produce an initial weak classifier and then use unlabeled data to improve the performance. Experiments based on two credit datasets reveal that co-training SVM&DT (Support Vector Machine & Decision Tree) gets the better performance than the compared methods. Based on these results, we can conclude that co-training SVM&DT can be used as an alternative method for enterprise credit risk assessment. © 2015 ICIC International.

关键词： Decision trees

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A Sophisticated RMPC Design for LPV Systems based on the mixed multi-step feedback control 34

A Sophisticated RMPC Design for LPV Systems based on the mix...

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第三十四届中国控制会议

作者： ZHENG Pengyuan LI Dewei XI Yugeng LI Xiaoli College of Automation Engineering Shanghai University of Electric PowerShanghai Engineering Research Center of Intelligent Management and Control for Power Process Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information Processing Ministry of Education School of Information Science and Technology Donghua University

ISBN: (纸本)9781467374439

This paper studies a sophisticated robust model predictive control(RMPC) design for linear parameter varying(LPV)systems with the varying parameter unmeasurable. Based on a mixed multi-step feedback control, a sophisticated control design which utilizes both multi-step control sets and parameter-dependent feedback control is developed to introduce more freedom and thus reduces the conservativeness. The proposed method achieves good control performance with guaranteed robust and stability properties, which is verified by simulation examples.

关键词： Linear parameter varying systems,Robust model predictive control,Parameter-dependent feedback control,Mixed multi-step feedback control,Linear matrix inequalities

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A study on DS-SVM-based forecast model from the view of information fusion

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International Journal of u- and e- Service, Science and Technology 2015年第2期8卷 395-404页

作者： Zhu, Weidong Sun, Yibo Wu, Yong Liu, Jingyu School of Economics Hefei University of Technology HefeiAnhui China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education HefeiAnhui China Institute of Industrial Information Hefei University of Technology HefeiAnhui China School of Management Hefei University of Technology HefeiAnhui China School of Management Hefei University of Technology HefeiAnhui China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education HefeiAnhui China School of Management Hefei University of Technology HefeiAnhui China

Dempster-Shafer Theory is specially advantaged in information fusion, while Support Vector Machine (SVM) can well deal with high-dimensional limited sample data. This Article firstly forecasts the data samples by categories with multiple SVMs, and hence based thereon, fuses the resulting information from multiple SVM models by using DS theory. At the end, Anderson's Iris data set is used to simulate the system of the created DS-SVM model, which shows that the approaches proposed in this Article can not only increase the accuracy rate of the SVM forecasting model, but also improve the propensity scores of the SVM forecasting model. © 2015 SERSC.

关键词： Support vector machines

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COS-training: A new semi-supervised learning method for keyphrase extraction based on co-training and SMOTE

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ICIC Express Letters, Part B: Applications 2015年第1期6卷 233-238页

作者： Wang, Gang Yang, Shanlin Ma, Jian School of Management Hefei University of Technology No. 193 Tunxi Road Hefei China Ministry of Education Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei University of Technology No. 193 Tunxi Road Hefei China Department of Information Systems City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong

As keyphrase is a small set of words that can best represent a document, they play significant roles in varieties of text-related tasks. In recent years, many unsupervised and supervised methods have been proposed for keyphrase extraction. However, keyphrase extraction is an imbalanced classification problem in nature and contains many unlabeled data, which have not been paid attention to in the previous studies. In this research, a new semi-supervised learning method, COS-training, is proposed for keyphrase extraction based on co-training and SMOTE. For the testing and illustration purpose, a keyphrase extraction dataset is selected to verify the effectiveness of the proposed method. Empirical results reveal that COS-training is a potential solution for keyphrase extraction. Among the compared methods, COS-training gets the best result. Al l these results illustrate that COS-training can be used as an alternative method for keyphrase extraction.

关键词： Extraction

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The green project of data management for industrial integration

International Journal of Database Theory and Application

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International Journal of Database Theory and Application 2015年第6期8卷 333-342页

作者： Tian, Yiming Zhu, keyu Pan, Ruoyu Liang, Changyong School of Computer and Information Hefei University of Technology Anhui Transport Interconnection Management Center Hefei China Institute of Computer Network Systems Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China Key Technologies and Equipment of IOT of Highway Traffic Anhui Engineering Technology Research Center Hefei China

The green project fulfills the requests of industrial management and application by sharing data rather than isolating. The data center with energy-efficient equipment is correspondingly effective in industrial service, management and construction of information. This study suggests a green data center in the two-level framework as a basic factor in industrial integration with potent procedures. In the case of transportation, it processed validating data to resolve logistics and emergency operations. © 2015 SERSC.

关键词： Information management

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Mid-term load forecasting based on modified grey model

Mid-term load forecasting based on modified grey model

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2013 International Conference on Mechatronics and Automatic Control Systems, ICMS 2013

作者： Wang, Haijiang Yang, Shanlin Key Lab of Process Optimization and Intelligent Decision-making Hefei University of Technology Hefei China School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9783319012728

The construction of smart grid put forward higher requirements on deployment accuracy of the energy. Power generation and electricity sectors have carried out more accurate data analysis and forecasting. In this context, we provide a Gauss-Chebyshev GM(1,1) model, This model could overcome the lack of traditional grey model and made accurate forecasting of electricity consumption in smart grid, Finally, numerical examples demonstrate that this method can efficiently improve the prediction accuracy. © Springer International Publishing Switzerland 2014.

关键词： Smart power grids

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