This article deals with the design of networks to be loaded on satellites. These networks should connect inputs (corresponding to signals arriving on the satellite) to outputs (corresponding to ampli ers), even in case of failures of ampli ers. They are made of links and expensive switches, hence we want to minimise the number of switches subject to the following conditions: each input and each...

Data Envelopment Analysis (DEA) is a powerful quantitative tool that provides a means to obtain useful information about efficiency and performance of firms, organizations, and all sorts of functionally similar, relatively autonomous operating units, known as Decision Making Units (DMU). Usually the investigated DMUs are characterized by a vector of multiple inputs and multiple outputs. Unfortu...

the ahp/dea methodology is an integration of analytical hierarchical process (ahp) and data envelopment analysis. this method uses the capabilities of both ahp and dea. however, it has some problems: it illogically compares two decision-making units in a data envelopment analysis (dea) model, it is not compatible with dea ranking in the case of multiple inputs/multiple outputs, and it leads to ...

This paper presents some analyses about the robust practical stability of a class of nonlinear affine systems in the presence of non-vanishing perturbations based on the passivity concept. The given analyses confirm the robust passivity property of the perturbed nonlinear systems in a certain region. Moreover, robust control laws are designed to guarantee the practical stability of the perturbe...

Data Envelopment Analysis (DEA) is an eciency measurement tool for evaluation of similar Decision Making Units (DMUs). In DEA, weights are assigned to inputs and outputs and the absolute eciency score is obtained by the ratio of weighted sum of outputs to weighted sum of inputs. In traditional DEA models, this measure is also equivalent with relative eciency score which evaluates DMUs in compar...

Data Envelopment Analysis (DEA) is a widely used technique for measuring the relative efficiencies of homogenous Decision Making Units (DMUs) with multiple inputs and multiple outputs. These factors may be evaluated in fuzzy or stochastic environment. Hence, the classic structures of DEA model may be changed where in two fold fuzzy stochastic environment. For instances, linearity, feasibility a...

  Data envelopment analysis (DEA) is a nonparametric approach to evaluate theefficiency of decision making units (DMU) using mathematical programmingtechniques. Almost, all of the previous researches in stochastic DEA have been usedthe stochastic data when the inputs and outputs are normally distributed. But, thisassumption may not be true in practice. Therefore, using a normal distribution wi...

radial models of dea to measure the efficiency of dmus assumed an inefficient unit to be efficient must decrease (increase) proportional all inputs (outputs) as the same. this assumption is absolutely false, unnecessary and unrealistic, therefore, to address this deficiency, and raise awareness of how to use resources, it is so realistic that expect the various inputs have different efficiency....

An experiment was performed to investigate the energy efficiency and effect of poultry house size on energy productivity in 3 different capacity management systems. Capacities of houses were 10000(3 housings), 20000 (2 housings) and 28000 (1 housing) birds per production period and were assigned as HI, HII and HIII respectively. This experiment was conducted in a completely randomized design ap...

Cherchye et al. (2013b) introduced a DEA methodology that is specially tailored for multi-output efficiency measurement. The methodology accounts for jointly used inputs and incorporates information on how inputs are allocated to outputs. In this paper, we present extensions that render the methodology useful to deal with undesirable (or “bad”) outputs in addition to desirable (or “good”) outpu...