This paper firstly builds a comprehensive evaluation of the development level of tourism industry about the cities of Anhui,then it uses AHP to determine the weight and TOPSIS method to analyze the development level o...
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This paper firstly builds a comprehensive evaluation of the development level of tourism industry about the cities of Anhui,then it uses AHP to determine the weight and TOPSIS method to analyze the development level of tourism industry about the cities of Anhui in 2004,2008 and 2012. The method of the Markov chain analyzes the time pattern evolution of the development level of tourism industry,and the spatial autocorrelation analysis of Arc GIS10.0 software explores the temporal-spatial evolution of the development level of tourism industry about the cities. The results show that the tourism industry of Anhui province has been initially formed agglomeration effect,the region's tourism industry development in southern Anhui is always strong,the growth of tourism industry development level in the middle area of Anhui is most prominent,and the region's tourism industry development in northern Anhui is always weak.
The data center Energy Consumption(EC) and the user Quality of Service(QoS) are two important influencing factors,which the cloud service provider must balance in the process of providing virtual cloud services resour...
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ISBN:
(纸本)9781479970186
The data center Energy Consumption(EC) and the user Quality of Service(QoS) are two important influencing factors,which the cloud service provider must balance in the process of providing virtual cloud services resources(VCSR).For the VCSR over-deployment phenomenon happening in cloud computing Infrastructure as a Service(IAAS),a user-oriented deployment constraint of VCSR is proposed,and the definition of user's Service Level Agreements(SLA) and the energy consumption of data center are also *** the basis of that,the model of dynamic migration of the virtual nodes is given,solved by simulated annealing *** the experiments,different strategies are compared using *** results show that the suggested strategy has advantages on balancing the energy consumption of data center and user's QoS.
More and more internet data centers (IDCs) are trying to use renewable energy sources (RESs). However, powering IDCs with intermittent RESs presents a significant challenge. In addition, power and workload management ...
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ISBN:
(纸本)9781665434263
More and more internet data centers (IDCs) are trying to use renewable energy sources (RESs). However, powering IDCs with intermittent RESs presents a significant challenge. In addition, power and workload management in IDCs has great potential to reduce energy consumption, carbon footprints and energy cost. This study proposes an optimal load dispatch model for an IDC with battery energy storage system (BESS), which aims to lower the total costs. To accommodate the uncertainties of wind power, a data-driven distributionally robust optimization (DDRO) method is adopted. Then the column-and-constraint generation method (C&CG) is used to solve the corresponding optimization problem. In the experiment with the real-world traces of workload arrival and wholesale electricity price, three scheduling scenarios are investigated. Moreover, robust optimization and stochastic programming are implemented for comparison. Experimental results reveal that total costs of the IDC can be effectively reduced by adopting BESS and implementing workload dispatch. Meanwhile, the results also demonstrate the effectiveness of DDRO approach.
In order to improve the accuracy of photovoltaic power output prediction, a photovoltaic power prediction method based on similar days and improved artificial bee colony support vector machine is proposed. Firstly, th...
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In order to improve the accuracy of photovoltaic power output prediction, a photovoltaic power prediction method based on similar days and improved artificial bee colony support vector machine is proposed. Firstly, through calculating the Euclidean distance of history day and measured day meteorological factors to determine similar days. Secondly, select historical data of photovoltaic power output, temperature, humidity and daily radiation on the slope of similar days and temperature, humidity and daily radiation on the slope of test date as input variables of support vector machine. And we adopt the improved artificial bees colony to optimize kernel function parameters and the penalty factor of support vector machine. Finally get the output in each period of photovoltaic power prediction. The experimental results showed that the proposed method can effectively improve the prediction accuracy of photovoltaic power.
Hesitant fuzzy set theory provides an effective technique for researchers and engineers to cope with vagueness and uncertainty. In recent years, to explore the correlation between hesitant fuzzy sets, traditional corr...
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Due to the impact of external objective factors, the weekly number of tourist distribution is imbalance and shows strong nonlinear characteristics, especially more obvious in the off-season. Aiming at this problem, th...
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The discrete Artificial Fish Swarm Algorithm (AFSA) has some defectives, such as falling into local optimum value, converging slowly. In order to overcome these shortcomings, an improved discrete optimization algorith...
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According to the lack of initial pheromone which leads to the long searching time of the Binary Ant Colony algorithm (BACO), a novel method named Binary Ant Colony Algorithm with Particle optimization Feature (PBACO) ...
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The selection of project management (PM) software is a complex issue and has significant impacts to the efficiency of the automobile research & development (R&D) process. Given two alternatives based on critic...
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ISBN:
(纸本)9781424451623;9781424451616
The selection of project management (PM) software is a complex issue and has significant impacts to the efficiency of the automobile research & development (R&D) process. Given two alternatives based on critical path method (CPM) and critical chain project management (CCPM) respectively, decision-makers usually cause confusion. This paper analyzed the characteristic of automobile R&D project and developed an evaluation model based on the fuzzy analytic hierarchy process (FAHP) and fuzzy multiple criteria decisionmaking (FMCDM) methods, which can help the managers in automobile industry to select more appropriate software in the course of product R&D. In particular, the FAHP method is used to obtain the weights of evaluation criteria, and the FMCDM method is used to determine the final rank of the software. The uncertainty and vagueness in evaluation procedure were presented as the fuzzy triangular numbers. The proposed method is applied to the case study of evaluating the performance of PM software based on CPM and CCPM respectively for a car manufacturer. The result of evaluation not only assisted managers in choosing suitable software but also outline the trend in development of CCPM software.
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