There is a general interest in ranking schemes applied to complex entities described by multiple attributes. Published rankings for universities are in great demand but are also highly controversial. We compare two classification and ranking schemes involving universities; one from a published report, "Top American Research Universities" by the University of Florida's TheCenter and the other using DEA. Both approaches use the same data and model. We compare the two methods and discover important equivalences. We conclude that the critical aspect in classification and ranking is the model. This suggests that DEA is a suitable tool for these types of studies.

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We present an algorithm for identifying the extreme rays of the conical hull of a finite set of vectors whose generated cone is pointed. This problem appears in diverse areas including stochastic programming, computational geometry, and non-parametric efficiency measurement. The standard approach consists of solving a linear program for every element of the set of vectors. The new algorithm differs in that it solves fewer and substantially smaller LPs. Extensive computational testing validates the algorithm and demonstrates that for a wide range of problems it is computationally superior to the standard approach.

The recourse function in a stochastic program with recourse can be approximated by separable functions of the original random variables or linear transformations of them. The resulting bound then involves summing simple integrals. These integrals may themselves be difficult to compute or may require more information about the random variables than is available. In this paper, we show that a special class of functions has an easily computable bound that achieves the best upper bound when only first and second moment constraints are available.