E-Portfolios for Team-Based and Project-Learning Based Marketing Classes

This e-portfolio describes the use of two EBTs including team-based learning and project-based learning in multiple quarters in an upper level marketing class. The document deals with the motivations of incorporating these practices and how students benefited from them

Group efficiency analysis in decision processes: a data envelopment analysis approach

Data envelopment analysis (DEA) is a powerful mathematical programming methodology for evaluating the relative efficiency of decision-making units (DMUs) with multiple outputs and multiple inputs. In the classic DEA, it has been implicitly assumed that all DMUs perform in a unique technology set and the traditional DEA cannot measure the relative performances of DMUs with dissimilar classes. In other words, if we have different groups of DMUs, the traditional DEA models cannot be applied to evaluate such cases. In this paper, it has been assumed that the DMUs do business in different groups. We are interested to evaluate the members of the groups. The main aim of this paper is proposing a DEA-based methodology to estimate the technical efficiency of DMUs along with different groups with different technologies. The proposed method is illustrated by an empirical example on banking industry

A Linear Programming Relaxation DEA Model for Selecting a Single Efficient Unit with Variable RTS Technology

The selection-based problem is a type of decision-making issue which involves opting for a single option among a set of available alternatives. In order to address the selection-based problem in data envelopment analysis (DEA), various integrated mixed binary linear programming (MBLP) models have been developed. Recently, an MBLP model has been proposed to select a unit in DEA with variable returns-to-scale technology. This paper suggests utilizing the linear programming relaxation model rather than the MBLP model. The MBLP model is proved here to be equivalent to its linear programming relaxation problem. To the best of the authors’ knowledge, this is the first linear programming model suggested for selecting a single efficient unit in DEA under the VRS (Variable Returns to Scale) assumption. Two theorems and a numerical example are provided to validate the proposed LP model from both theoretical and practical perspectives

Estimating outputs using an inverse non-radial model with non-discretionary measures: An application for restaurants

Few inverse data envelopment analysis (DEA) models have incorporated non-discretionary measures based on radial efficiency values. However, the efficiency may be miscounted in radial approaches when some non-zero slacks appear. Furthermore, there is scant research on inverse DEA to estimate performance measures in the restaurant industry. Accordingly, this research proposes models based on non-radial DEA to analyze the efficiency and output changes of some Iranian restaurants while also presenting non-discretionary measures. Actually, in the company of non-discretionary factors, a non-radial DEA approach and its inverse problem are introduced to assess the performance and estimate the outputs for the modifications of inputs, respectively, while the inefficiency levels are maintained (and when they are preserved or decreased). The inefficiency of each discretionary input and output is specified using the presented non-radial DEA approach, and output targets are determined through inverse non-radial DEA with non-discretionary inputs. The results show containing non-discretionary data leads to more rational determinations through non-radial DEA-founded problems. This research presents analytic insights into the resources of inefficiency and output targets of entities with non-discretionary data, such as restaurants.

Regional flood frequency analysis using an artificial neural network model

This paper presents the results from a study on the application of an artificial neural network (ANN) model for regional flood frequency analysis (RFFA). The study was conducted using stream flow data from 88 gauging stations across New South Wales (NSW) in Australia. Five different models consisting of three to eight predictor variables (i.e., annual rainfall, drainage area, fraction forested area, potential evapotranspiration, rainfall intensity, river slope, shape factor and stream density) were tested. The results show that an ANN model with a higher number of predictor variables does not always improve the performance of RFFA models. For example, the model with three predictor variables performs considerably better than the models using a higher number of predictor variables, except for the one which contains all the eight predictor variables. The model with three predictor variables exhibits smaller median relative error values for 2- and 20-year return periods compared to the model containing eight predictor variables. However, for 5-, 10-, 50- and 100-year return periods, the model with eight predictor variables shows smaller median relative error values. The proposed ANN modelling framework can be adapted to other regions in Australia and abroad

Prioritization method for frontier DMUs: a distance-based approach

In nonparametric methods, if the number of observations is relatively small as compared to the sum of number of inputs and outputs, many units are evaluated as efficient. Several methods for prioritizing these efficient units are reported in literature. Andersen et al. and Mehrabian et al. proposed two methods for ranking efficient units, but both methods break down in some cases. This paper describes a new DEA ranking approach that uses L2-norm

An Improvement to The Relative Efficiency With Price Uncertainty: An Application to The Bank Branches

Abstract This article describes the method of measuring relative efficiency, when the input and output prices are unknown. In a situation, where only the bound of input prices for the cost efficiency and the bound of output prices for the revenue efficiency are known, measurement of relative efficiency consists of two cases: optimistic and pessimistic perspective. The main object of this article is to study the pessimistic relative efficiency that eventually, with the computation of assessment of optimistic, it gives an interval efficiency for each DMU. Finally we apply the method in the analysis of bank branches activity