Data Envelopment Analysis (DEA) is a nonparametric tool for discriminating the best performers from a number of homogenous Decision Making Units (DMU). Cost oriented DEA models identify those best DMUs which run cost efficient process. This paper validates the outcome derived from the Ideal Frontier (mentioned in Sarkar. S (2014)) derived from non-central Principal Component Analysis and a slac...

This paper considers the job scheduling problem in virtual manufacturing cells (VMCs) with the goal of minimizing two objectives namely, makespan and total travelling distance. To solve this problem two algorithms are proposed: traditional non-dominated sorting genetic algorithm (NSGA-II) and knowledge-based non-dominated sorting genetic algorithm (KBNSGA-II). The difference between these algor...

<p style='text-indent:20px;'>The conventional data envelopment analysis (DEA) models presume that the values of input-output variables decision-making units (DMUs) are precisely known. However, some real-life situations can authoritatively mandate to vary in concrete fine-tuned ranges, which include negative and measures allowed take integer only. Our study proposes an integrated dynamic ...

Data envelopment analysis (DEA) is a non-parametric mathematical programming methodology used to not only evaluate the relative efficiencies of comparable decision making units (DMUs) with multiple inputs and outputs, but also provide efficiency targets for the DMUs. For a visualization of this method, see Figure 1. Since the incept of Charnes, Cooper and Rhodes (1978), DEA has gained increasin...

Keywords: Fuzzy DEA Intuitionistic fuzzy DEA Optimistic and pessimistic performance Ranking methods Bank branch efficiency a b s t r a c t Intuitionistic fuzzy set (IFS) is an extension of fuzzy set and an approach to define a fuzzy set where available information is not sufficient to define an imprecise concept by means of a conventional fuzzy set. The existing fuzzy DEA (FDEA) models for meas...

We develop a new Data Envelopment Analysis (DEA)-based methodology for measuring the efficiency of Decision Making Units (DMUs) characterized by multiple inputs and multiple outputs. The distinguishing feature of our method is that it explicitly includes information about output-specific inputs and joint inputs in the efficiency evaluation. This contributes to opening the " black box " of effic...

Traditional cost-efficiency analysis methods require exact and precise values for inputs, outputs and input prices. However, this is not the case in many real-life applications. This study proposes a rough costefficiency approach to the problem of ranking efficient decision making units (DMUs). Based on rough set theory, a nonparametric methodology for cost-efficiency analysis is developed. The...

Ranking efficiency based on DEA results can be used for grouping DMUs. The resulting group membership can be partly related to environmental characteristics of DMU, which are not used either as input or output. Utilizing the expert knowledge on super efficiency DEA results, we propose a multinomial Dirichlet regression model which can be used for the purpose of selection of new projects. A case...