996 resultados para network benefits


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In this paper a p--median--like model is formulated to address theissue of locating new facilities when there is uncertainty. Severalpossible future scenarios with respect to demand and/or the travel times/distanceparameters are presented. The planner will want a strategy of positioning thatwill do as ``well as possible'' over the future scenarios. This paper presents a discrete location model formulation to address this P--Medianproblem under uncertainty. The model is applied to the location of firestations in Barcelona.

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In most firms, managers periodically assess workers' performance. Evidence suggeststhat managers withhold information during these reviews, and some observersargue that this necessarily reduces surplus. This paper assesses the validity of thisargument when workers have career concerns. Disclosure has two effects: it exposesthe worker to uncertainty about future effort levels, but allows him to use current effortto influence his employer's beliefs about future effort. The surplus-maximizingdisclosure policy reveals output realizations in the center of the distribution, butnot in the tails. Thus, it is efficient for firms to reveal some but not all performanceinformation.

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We develop an equilibrium search-matching model with risk-neutral agentsand two-sided ex-ante heterogeneity. Unemployment insurance has thestandard effect of reducing employment, but also helps workers to get a suitable job. The predictions of our simple modelare consistent with the contrasting performance of the labor market in Europeand US in terms of unemployment, productivity growth and wage inequality.To show this, we construct two fictitious economies with calibratedparameters which only differ by the degree of unemployment insurance andassume that they are hit by a common technological shock which enhancesthe importance of mismatch. This shock reduces the proportion of jobs whichworkers regards as acceptable in the economy with unemployment insurance(Europe). As a result, unemployment doubles in this economy.In the laissez-faire economy (US), unemployment remains constant,but wage inequality increases more and productivity grows less due to largermismatch. The model can be used to address a number of normative issues.

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We argue the importance both of developing simple sufficientconditions for the stability of general multiclass queueing networks and also of assessing such conditions under a range of assumptions on the weight of the traffic flowing between service stations. To achieve the former, we review a peak-rate stability condition and extend its range of application and for the latter, we introduce a generalisation of the Lu-Kumar network on which the stability condition may be tested for a range of traffic configurations. The peak-rate condition is close to exact when the between-station traffic is light, but degrades as this traffic increases.

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Audit report on the Iowa Communications Network (ICN) for the year ended June 30, 2007

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Agency Performance Plan, Iowa Communications Network

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The Network Revenue Management problem can be formulated as a stochastic dynamic programming problem (DP or the\optimal" solution V *) whose exact solution is computationally intractable. Consequently, a number of heuristics have been proposed in the literature, the most popular of which are the deterministic linear programming (DLP) model, and a simulation based method, the randomized linear programming (RLP) model. Both methods give upper bounds on the optimal solution value (DLP and PHLP respectively). These bounds are used to provide control values that can be used in practice to make accept/deny decisions for booking requests. Recently Adelman [1] and Topaloglu [18] have proposed alternate upper bounds, the affine relaxation (AR) bound and the Lagrangian relaxation (LR) bound respectively, and showed that their bounds are tighter than the DLP bound. Tight bounds are of great interest as it appears from empirical studies and practical experience that models that give tighter bounds also lead to better controls (better in the sense that they lead to more revenue). In this paper we give tightened versions of three bounds, calling themsAR (strong Affine Relaxation), sLR (strong Lagrangian Relaxation) and sPHLP (strong Perfect Hindsight LP), and show relations between them. Speciffically, we show that the sPHLP bound is tighter than sLR bound and sAR bound is tighter than the LR bound. The techniques for deriving the sLR and sPHLP bounds can potentially be applied to other instances of weakly-coupled dynamic programming.

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We use network and correspondence analysis to describe the compositionof the research networks in the European BRITE--EURAM program. Our mainfinding is that 27\% of the participants in this program fall into one oftwo sets of highly ``interconnected'' institutions --one centered aroundlarge firms (with smaller firms and research centers providing specializedservices), and the other around universities--. Moreover, these ``hubs''are composed largely of institutions coming from the technologically mostadvanced regions of Europe. This is suggestive of the difficulties of attainingEuropean ``cohesion'', as technically advanced institutions naturally linkwith partners of similar technological capabilities.

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The network choice revenue management problem models customers as choosing from an offer-set, andthe firm decides the best subset to offer at any given moment to maximize expected revenue. The resultingdynamic program for the firm is intractable and approximated by a deterministic linear programcalled the CDLP which has an exponential number of columns. However, under the choice-set paradigmwhen the segment consideration sets overlap, the CDLP is difficult to solve. Column generation has beenproposed but finding an entering column has been shown to be NP-hard. In this paper, starting with aconcave program formulation based on segment-level consideration sets called SDCP, we add a class ofconstraints called product constraints, that project onto subsets of intersections. In addition we proposea natural direct tightening of the SDCP called ?SDCP, and compare the performance of both methodson the benchmark data sets in the literature. Both the product constraints and the ?SDCP method arevery simple and easy to implement and are applicable to the case of overlapping segment considerationsets. In our computational testing on the benchmark data sets in the literature, SDCP with productconstraints achieves the CDLP value at a fraction of the CPU time taken by column generation and webelieve is a very promising approach for quickly approximating CDLP when segment consideration setsoverlap and the consideration sets themselves are relatively small.

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This report outlines the strategic plan for Iowa Communications Network, goals and mission.

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This is the Annual Report for Fiscal Year 2007 (July 1, 2007-June 30, 2008) for the Iowa Communications Network.

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Iowa Code § 8D.10 requires certain state agencies prepare an annual report to the General Assembly certifying the identified savings associated with that state agency’s use of the Iowa Communications Network (ICN). This report covers estimated cost savings related to video conferencing via ICN for the Iowa Department of Transportation (DOT). In FY 2008, the DOT did not conduct any sessions utilizing ICN’s video conferencing system. Therefore, no cost savings were calculated for this report.

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The objective of this paper is to compare the performance of twopredictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross validation, leave-one-out and three different bootstrap algorithms. The final results of each model were compared with error rate and the area under receiver operating characteristic curves (Az). The neural network obtained statistically higher Az than LR with cross validation. The remaining resampling validation methods did not reveal statistically significant differences between LR and NN rules. The neural network classifier performs better than the one based on logistic regression. This advantage is well detected by three-fold cross-validation, but remains unnoticed when leave-one-out or bootstrap algorithms are used.

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This paper analyses the application of hybrid risk adjustment versus either prospective orconcurrent risk adjustment formulae in the context of funding pharmaceutical benefits for thepopulation of an integrated healthcare delivery organization in Catalonia during years 2002 and2003. We apply a mixed formula and find that a hybrid risk adjustment model increasesincentives for efficiency in the provision of low risk individuals at health organizations not only asa whole but also at each internal department compared to only prospective models by reducingwithin-group variation of drug expenditures.

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O trabalho científico realizado no âmbito dos regulamentos dos cursos de graduação para a obtenção de grau de licenciatura na Universidade Jean Piaget de Cabo Verde, pretende analisar a utilização da VPN, mais concretamente na Universidade Jean Piaget de Cabo Verde. Durante a elaboração desse trabalho será feito uma breve introdução sobre redes de computadores, sobre segurança na rede, um enquadramento teórico sobre VPN, bem como a sua contextualização num ambiente organizacional, mostrando as várias vantagens do seu uso, custo e beneficio e por fim o caso prático na Universidade Jean Piaget.