Two-machine no-wait flowshop scheduling with learning effect and convex resource-dependent processing times

Two-machine no-wait flowshop scheduling problems in which the processing time of a job is a function of its position in the sequence and its resource allocation are considered in the study. The primary objective is to find the optimal sequence of jobs and the optimal resource allocation separately. Here we propose two separate models: minimizing a cost function of makespan, total completion time, total absolute differences in completion times and total resource cost; minimizing a cost function of makespan, total waiting time, total absolute differences in waiting times and total resource cost. Since each model is strongly NP-hard, we solve both models by breaking them down to two sub-problems, the optimal resource allocation problem for any job sequence and the optimal sequence problem with its optimal resource allocation. Specially, we transform the second sub-problem into the minimum of the bipartite graph optimal matching problem (NP-hard), and solve it by using the classic KM (Kuhn–Munkres) algorithm. The solutions of the two sub-problems demonstrate that the target problems remain polynomial solvable under the proposed model.     

A survey on resource allocation techniques in OFDM(A) networks

In this contribution, we present a survey on the radio resource allocation techniques in orthogonal frequency division multiplexing (OFDM) and orthogonal frequency division multiple access (OFDMA) systems. This problem goes back to 1960s and that is related to properly and efficiently allocate the radio resources, namely subcarriers and power. We start by overviewing the main open issues in OFDM. Then, we describe the problem formulation in OFDMA, and we review the existing solutions to allocate the radio resources. The goal is to discuss the fundamental concepts and relevant features of different radio resource management criteria, including water-filling, max–min fairness, proportional fairness, cross-layer optimization, utility maximization, and game theory, also including a toy example with two terminals to compare the performance of the different schemes. We conclude the survey with a review of the state-of-the-art in resource allocation for next-generation wireless networks, including multicellular systems, cognitive radio, and relay-assisted communications, and we summarize advantages and common problems of the existing solutions available in the literature. The distinguishing feature of this contribution is a tutorial-style introduction to the fundamental problems in this area of research, intended for beginners on this topic.     

Cokriging-Based Sequential Design Strategies Using Fast Cross-Validation Techniques for Multi-Fidelity Computer Codes

Cokriging-based surrogate models have become popular in recent decades to approximate a computer code output from a few simulations using both coarse and more complex versions of the code. In practical applications, it is common to sequentially add new simulations to obtain more accurate approximations. We propose a method of cokriging-based sequential design, which combines both the error evaluation provided by the cokriging model and the observed errors of a leave-one-out cross-validation procedure. This method is proposed in two versions, the first one selects points one at a time. The second one allows us to parallelize the simulations and to add several design points at a time. The main advantage of the suggested strategies is that at a new design point they choose which code versions should be simulated (i.e., the complex code or one of its fast approximations). A multifidelity application is used to illustrate the efficiency of the proposed approaches. In this example, the accurate code is a two-dimensional finite element model and the less accurate one is a one-dimensional approximation of the system. This article has supplementary material online.     

Key based data analytics across data centers considering bi-level resource provision in cloud computing

Due to the distribution characteristic of the data source, such as astronomy and sales, or the legal prohibition, it is not always practical to store the world-wide data in only one data center (DC). Hadoop is a commonly accepted framework for big data analytics. But it can only deal with data within one DC. The distribution of data necessitates the study of Hadoop across DCs. In this situation, though, we can place mappers in the local DCs, where to place reducers is a great challenge, since each reducer needs to process almost all map output across all involved DCs. In this paper, a novel architecture and a key based scheme are proposed which can respect the locality principle of traditional Hadoop as much as possible while realizing deployment of reducers with lower costs. Considering both the DC level and the server level resource provision, bi-level programming is used to formalize the problem and it is solved by a tailored two level group genetic algorithm (TLGGA). The final results, which may be dispersed in several DCs, can be aggregated to a designative DC or the DC with the minimum transfer and storage cost. Extensive simulations demonstrate the effectiveness of TLGGA. It can outperform both the baseline and the state-of-the-art mechanisms by 49% and 40%, respectively.     

whittle软件境界优化设计的理念与技术方法

介绍了国外设计咨询公司处理矿石损失贫化及构建采矿模型的理念与方法,并对whittle软件优化境界的概念及技术进行了详细论证。     

Robust resource adequacy planning in the face of coal retirements

We investigate the resource adequacy requirements of the PJM Interconnection, and the sensitivity of capacity procurement decisions to the choice of reliability metric used to measure resource adequacy. Assuming that plants fail independently, we find that PJM's 2010 reserve margin of 20.5% was sufficient to achieve the stated reliability standard of one loss of load event per ten years, with 0.012 expected loss of load events per year. PJM could reduce reserve margins to 13% and still achieve adequate levels of reliability as measured by the 2.4 Loss of Load Hours metric and the 0.001% Unserved Energy metric, which are used by other U.S. and international systems. A reserve margin of 13–15% would minimize long-run system costs. Reducing reserve margins from 20.5% to 13% in 2010 would have reduced PJM's capacity procurement by 11 GW, the same amount of coal capacity that PJM has identified as at high risk of retirement. We also investigate the risk posed by correlated failures among generators, a risk traditionally not modeled by system planners. We illustrate that three types of correlated failures may increase outage risks: natural gas supply disruptions, reduced reliability among generators during winter months, and the simultaneous shutdown of multiple nuclear generators for regulatory reasons.     

Market distortions and aggregate productivity: Evidence from Chinese energy enterprises

Market distortions can generate resource misallocations across heterogeneous firms and reduce aggregate productivity. This paper measures market distortions and aggregate productivity growth in China's energy sector. We use the wedge between output elasticities and factor shares in revenues to recover a measure of firm-level market distortions. Using data on a large sample of Chinese energy enterprises from 1999 to 2007, our estimations provide strong evidence of the existence of both factor and product market distortions within and across China's various energy industries. The productivity aggregation and decomposition results demonstrate that the estimated aggregate productivity growth (APG) is, on average, 2.595% points per year, of which technological change, resource reallocation, and firm entries and exits account for 1.981, 0.068, and 0.546% points, respectively. The weak contributions of resource reallocation and firm turnover to APG are also found in energy sub-industries, except in the coal industry. Our research suggests that China's energy sector has major potential for productivity gains from resource reallocation through the reduction of market distortions.     

Causal links between renewable energy,environmental degradation and economic growth in selected SAARC countries: Progress towards green economy

The objective of the study is to investigate the short-run and long-run causality relationship among energy (electricity production from renewable sources), carbon dioxide emissions, natural resource depletion, Gross Domestic Product (GDP) and poverty in selected SAARC countries, namely, Bangladesh, India, Nepal, Pakistan and Sri Lanka, over a period of 1975–2010. The results show that there is bidirectional Granger causality between carbon dioxide emissions & natural resource depletion in Nepal and between energy production & poverty in Pakistan. For the other three countries, the Granger causality runs from energy production to poverty in Bangladesh and India, and from poverty to energy production in Sri Lanka. The results of panel group Fully Modified OLS (FMOLS) indicates that GDP and poverty has a positive impact while carbon dioxide emission has a negative impact on energy production. Similarly, an increase in energy production leads to decrease in carbon emissions, where as, natural resource depletion increase carbon emissions in selected SAARC countries. Subsequently, an increase in energy production leads to increase in GDP which further increase carbon dioxide emission in SAARC region.     

A resource elasticity framework for QoS-aware execution of cloud applications

Cloud infrastructures consisting of heterogeneous resources are increasingly being utilized for hosting large-scale distributed applications from diverse users with discrete needs. The multifarious cloud applications impose varied demands for computational resources along with multitude of performance implications. Successful hosting of cloud applications necessitates service providers to take into account the heterogeneity existing in the behavior of users, applications and system resources while respecting the user’s agreed Quality of Service (QoS) criteria. In this work, we propose a QoS-Aware Resource Elasticity (QRE) framework that allows service providers to make an assessment of the application behavior and develop mechanisms that enable dynamic scalability of cloud resources hosting the application components. Experimental results conducted on the Amazon EC2 cloud clearly demonstrate the effectiveness of our approach while complying with the agreed QoS attributes of users.     

From dynamic capabilities to ERP enabled business improvements: The mediating effect of the implementation project

This study explores whether an Enterprise Resource Planning (ERP) implementation project mediates the relationship between dynamic pre-adoption capabilities and ERP-enabled business improvements. The results from our field survey of large ERP adopters in Austria suggest that the effects of the two out of three selected dynamic capabilities are dependent on the properties of the underlying organizational transformation project. While external information acquisition and IT governance capabilities are fully mediated by the performance of the ERP implementation project, decision making only directly impacts business capabilities. These results further our understanding about the role of the implementation project for ERP value creation and the different natures of capability relationships. We call for more research on co-presence conditions related to dynamic capabilities and IT transformation project performance.