Ranking Intervals and Dominance Relations for Ratio-Based Efficiency Analysis

We develop comparative results for ratio-based efficiency analysis, based on the decision making units' (DMUs) relative efficiencies over sets of feasible weight that characterize preferences for input and output variables. Specifically, we determine (i) ranking intervals which indicate the best and worst efficiency rankings that a DMU can attain relative to other DMUs, (ii) dominance structures which convey what other DMUs a given DMU dominates in one-on-one efficiency comparisons, and (iii) efficiency bounds which show how much more efficient a DMU can be relative to a given DMU or a subset of other DMUs. These efficiency results-which reflect the full range of feasible input and output weights-are robust in the sense that they are insensitive to possible outliers and do not necessitate particular returns-to-scale assumptions. We also report a real case study where these results supported the efficiency analysis of the twelve departments at a large technical university. Key words : performance measurement, data efficiency analysis, preference modeling Introduction Inspired by the seminal paper of Because the efficiency scores are computed relative to the efficiency frontier, these scores are potentially sensitive to what DMUs are included in or excluded from the analysis: specifically, the introduction/removal of a single outlier (e.g., an exceptionally efficient DMU that produces more outputs per inputs than the other DMUs) may shift the efficient frontier considerably, which may disrupt the reported efficiency scores for other DMUs and hence perplex the users of efficiency results (see, e.g., Seiford and Zhu, 1998ab; Motivated by the above considerations, we develop efficiency results which allow us to answer questions such as: • What are the best/worst rankings that DMU A can attain in comparison with other DMUs, based on their efficiency ratios

An interactive ranking-based multi-criteria choice algorithm with filtering: Applications to university selection

In this study, we develop an interactive algorithm to converge to the most preferred alternative of a decision maker (DM) among a set of discrete alternatives. The algorithm presents a limited number of alternatives to the DM and collects preference ranking of them iteratively. The preferences are modeled by a flexible and realistic preference function. To improve the performance, the alternatives presented are determined by a filtering method. We compare our algorithm with benchmark algorithms on numerous data sets from Quacquarelli Symonds, a higher education marketing company that reports annual rankings of universities under different categories. The results show that our algorithm outperforms the benchmark algorithms.Publisher's Versio

Continuous Decision Support

Organizations are often faced with portfolio construction efforts that require them to select one or more alternatives, subject to resource constraints, with the aim of achieving the maximum value possible. This is a well-defined problem with a number of analytically defensible approaches, provided the entire set of alternatives is known when the decision event takes place. Less well treated in the literature is how to approach this problem when the entire set of alternatives is unknown, as when the alternatives arrive over time. This change in the availability of data shifts the problem from one of identifying an optimal subset to one in which a series of smaller decisions are undertaken regarding the acceptability of each alternative as it presents itself. This work expands upon a methodology known as the Triage Method. The original Triage Method provided a screening tool that could be applied to alternatives as they presented themselves to determine if they should be accepted for further study, rejected out of hand, or held pending until later date. This decision was made strictly upon the value of the alternative and with no consideration of its cost. Two extensions to the Triage Method are offered which provide a capability to consider cost and other resource requirements of the alternatives, thus allowing a move from simply screening to portfolio selection. Guidelines are presented as to when each of these extensions is best employed, a characterization of the performance tradeoff between these and more traditional methodologies is developed, and insight and techniques for setting the value of parameters required by the extensions are provided

A flexible approach to ranking with an application to MBA Programs

We develop a model for flexibly ranking multi-dimensional alternatives/units into preference classes via Mixed Integer Programming. We consider a linear aggregation model, but allow the criterion weights to vary within pre-specified ranges. This allows the individual alternatives/units to play to their strengths. We illustrate the use of the model by considering the Financial Times Global MBA Program rankings and discuss the implications. We argue that in many applications neither the data nor the weights or the aggregation model itself is precise enough to warrant a complete ranking, providing an argument for sorting or what we call flexible ranking

A flexible approach to ranking with an application to MBA Programs

We develop a model for flexibly ranking multi-dimensional alternatives/units into preference classes via Mixed Integer Programming. We consider a linear aggregation model, but allow the criterion weights to vary within pre-specified ranges. This allows the individual alternatives/units to play to their strengths. We illustrate the use of the model by considering the Financial Times Global MBA Program rankings and discuss the implications. We argue that in many applications neither the data nor the weights or the aggregation model itself is precise enough to warrant a complete ranking, providing an argument for sorting or what we call flexible ranking.Ranking Sorting Mixed Integer Programming Multiple criteria MBA Programs

A sociology of rankings: a longitudinal examination of the Financial Times MBA rankings

Although rankings have been in existence for almost a century, scholars have only developed a limited understanding of the role of such aggregations of performance measures in organisational fields. In particular, there is little understanding of how cognitive and strategic processes at the organisational level, which underlie the measurement and ranking process, impact on macro outcomes on the level of the organisational field. This is due to the persistence of two assumptions about the impact of rankings on organisations and fields. These are, firstly, that here is a cause-effect relationship between the emergence of rankings and change processes in organisational fields, and, secondly, that those processes lead to the emergence of a single model of organising around which organisational fields and actors homogenise. Refuting these assumptions, I develop the concepts of the ranking template and frame to explain the ways in which rankings can impact on organisations and fields, given the dynamics between organisations that produce and those who are subject to rankings. In particular, I show that through ranking frames, the latter develop notions of actual and aspired ranking performances and a strategy to align the two. An in-depth analysis of a novel longitudinal data set from the Financial Times Global MBA rankings as well as data from business schools provides evidence of the multitude of ways in which rankings can impact on organisations and fields. Specifically, I show how ranking fields are made up of field boundaries, vertical segments of competitors, horizontal segments of different performance measures, as well as clusters of peers. I conclude by discussing the contribution of these findings to the understanding of the dynamics between producers and subjects of performance measures in organisational fields