Anchor points in DEA

Abstract. Anchor points play an important role in DEA theory and application. They define the transition from the efficient frontier to the "free-disposability" portion of the boundary. Our objective is to use the geometrical properties of anchor points to design and test an algorithm for their identification. We focus on the variable returns to scale production possibility set; our results do not depend on any particular DEA LP formulation, primal/dual form or orientation. Tests on real and synthetic data lead to unexpected insights into their role in the geometry of the DEA production possibility set

Bounding separable recourse functions with limited distribution information

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.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44185/1/10479_2005_Article_BF02204821.pd

Performance evaluation based on multiple attributes with nonparametric frontiers

Performance rating and comparison of a group of entities is frequently based on the values of several attributes. Such evaluations are often complicated by the absence of a natural or obvious way to weight the importance of the individual dimensions of the performance. This paper proposes a framework based on nonparametric frontiers to rate and classify entities described by multiple performance attributes into 'performers' and 'underperformers'. The method is equivalent to Data Envelopment Analysis (DEA) with entities defined only by outputs. In the spirit of DEA, the weights for each attribute are selected to maximize each entity's performance score. This approach, however, results in a new linear program that is more direct and intuitive than traditional DEA formulations. The model can be easily understood and interpreted by practitioners since it conforms better to the practice of evaluating and comparing performance using standard specifications. We illustrate the model's use with two examples. The first evaluates the performance of employees. The second is an application in manufacturing where multiple quality attributes are used to assess and compare performance of different manufacturing processes.Performance evaluation Data Envelopment Analysis (DEA) Linear programming

Identifying hospital antimicrobial resistance targets via robust ranking

<p>We develop a robust ranking procedure to uncover trends in variation in antibiotic resistance (AR) rates across hospitals for some antibiotic-bacterium pairs over several years. We illustrate how the method can be used to detect potentially dangerous trends and to direct attention to hospitals’ management practices. A robust method is indicated because some unusual reported resistance rates may be due to measurement protocol differences and not any real difference in AR rates. Our proposed method is less sensitive to outlier observations than other robust methods. The application on real AR data shows how a dangerous trend in a particular AR rate would be detected. Our results indicate the potential benefits of systematic AR rate collection and AR reporting systems across hospitals.</p