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The paper deals with models and methods for evaluation of efficiency of production units. The standard modeling approach for evaluation of efficiency is data envelopment analysis (DEA) based on the definition of efficiency as the ratio of outputs produced by the unit and inputs spent in the production process. Standard data envelopment analysis models divide the units into inefficient and efficient ones. The efficient units receive the efficiency score 100% by standard DEA models and can be further classified by so called super-efficiency DEA models. The paper discusses the possibility of using the AHP model with interval pairwise comparisons for evaluation and classification of efficient units and compares given1results with super-efficiency DEA scores. The proposed approach is applied in assessing the efficiency of pension funds in the Czech Republic-the results given by super-efficiency DEA models and by the interval AHP model are compared and discussed.

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Data envelopment analysis models usually split decision making units into two basic groups, efficient and inefficient. Efficiency score of inefficient units allows their ranking but efficient units cannot be ranked directly because of their maximum efficiency. That is why there are formulated several models for ranking of efficient units. The paper presents two original models for ranking of efficient units in data envelopment analysis-they are based on multiple criteria decision making techniques-goal programming and analytic hierarchy process. The first model uses goal programming methodology and minimizes either the sum of undesirable deviations or maximal undesirable deviation from the efficient frontier. The second approach is analytic hierarchy process model for ranking of efficient units. The two presented models are compared with several super-efficiency models and other approaches for ranking decision making units in DEA models including definitions based on distances from optimistic and pessimistic envelopes and cross efficiency evaluation models. The results of the analysis by all presented models are illustrated on a real data setevaluation of 194 bank branches of one of the Czech commercial banks.

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The purpose of this paper is to draw the first lessons from the on-going coronavirus crisis and to identify viable solutions for what should become the goal of any country: transforming their own economies into sensitive and responsive economies regarding public health problems. The originality of our approach is given by its objective as well as the strategy employed for verifying research hypotheses. The objective is twofold: detecting the indicators that may constitute signals for the vulnerability of countries in times of health crisis and highlighting the underlying factors of the resilience capacity. Many indicators have been considered: six indicators concerning Covid-19 pandemic and 27 socio-economic indicators. Three main hypotheses have been formulated and tested using various statistical methods. Our findings provide deep insights for understanding how Covid-19 crisis is correlated to specific economic (urbanization, sectorial employment, health system) and demographic factors (aging, mortality). The study has succeeded in identifying the pattern of a country with greater resilience and better ability to cope with a health crisis. Our results could be useful when forecasting the spread of another pandemic wave, its impact on people’s health and estimating how some markets will be reshaped.