Filling vacancies: Identifying the most efficient recruitment channel

This paper provides new results on the recruitment behaviour of firms by showing that recruitment channels clearly affect the probability of filling a vacancy. Using a French data, we estimate the effectiveness of different recruitment strategies, taking into account that recruitment channels choices are endogenous and that firms strategically combine several channels. Our results enable us to conclude that institutional intermediaries, such as private and public employment agencies, are found to be the most effective channels for firms. However, these intermediaries are specialized, in that private agencies are more efficient at filling skilled vacancies, whereas public agencies are more efficient at filling non-skilled vacancies.recruitment strategies, job vacancy

Screening and metamodeling of computer experiments with functional outputs. Application to thermal-hydraulic computations

To perform uncertainty, sensitivity or optimization analysis on scalar variables calculated by a cpu time expensive computer code, a widely accepted methodology consists in first identifying the most influential uncertain inputs (by screening techniques), and then in replacing the cpu time expensive model by a cpu inexpensive mathematical function, called a metamodel. This paper extends this methodology to the functional output case, for instance when the model output variables are curves. The screening approach is based on the analysis of variance and principal component analysis of output curves. The functional metamodeling consists in a curve classification step, a dimension reduction step, then a classical metamodeling step. An industrial nuclear reactor application (dealing with uncertainties in the pressurized thermal shock analysis) illustrates all these steps

Assessment of a diagnostic procedure for the monitoring and control of industrial processes

The definition of “energy efficiency” entails programming, planning and implementation of operational tools and strategies leading to the reduction of energy demand for the same offered services. Among the typical industrial energy uses, the production of compressed air represents certainly an important segment of potential saving. The present work studies the monitoring of the compressed air used for blow moulding of a packaging solution company. The study addresses the monitoring of compressed air line in term of operational and energy variables. The available measured data are used to evaluate the energy performance evolution during a year time. The work tackles the problem with two different approaches based on univariate and multivariate methods. The first method aims at finding a key performance index and a new univariate control chart related to energy/operational parameters to better monitor the performance of the compressed air plant. Besides, the multivariate analysis of the production process is applied in order to analyse the energy efficiency by also considering the multiple variables influencing the whole process itself. Final purposes are identify a new methodology for the production process analysis and evaluate flaws and strengths of these models

Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario

A variety of methods is available to quantify uncertainties arising with\-in the modeling of flow and transport in carbon dioxide storage, but there is a lack of thorough comparisons. Usually, raw data from such storage sites can hardly be described by theoretical statistical distributions since only very limited data is available. Hence, exact information on distribution shapes for all uncertain parameters is very rare in realistic applications. We discuss and compare four different methods tested for data-driven uncertainty quantification based on a benchmark scenario of carbon dioxide storage. In the benchmark, for which we provide data and code, carbon dioxide is injected into a saline aquifer modeled by the nonlinear capillarity-free fractional flow formulation for two incompressible fluid phases, namely carbon dioxide and brine. To cover different aspects of uncertainty quantification, we incorporate various sources of uncertainty such as uncertainty of boundary conditions, of conceptual model definitions and of material properties. We consider recent versions of the following non-intrusive and intrusive uncertainty quantification methods: arbitary polynomial chaos, spatially adaptive sparse grids, kernel-based greedy interpolation and hybrid stochastic Galerkin. The performance of each approach is demonstrated assessing expectation value and standard deviation of the carbon dioxide saturation against a reference statistic based on Monte Carlo sampling. We compare the convergence of all methods reporting on accuracy with respect to the number of model runs and resolution. Finally we offer suggestions about the methods' advantages and disadvantages that can guide the modeler for uncertainty quantification in carbon dioxide storage and beyond