the motivation of this paper is to propose such equitable method for ranking all decision making units (dmus) in dynamic data envelopment analysis (ddea) framework. as far as we are aware there is not more studies in dynamic dea literature. what's more, in such cases the best operating unit is important to be sampled for the others in under evaluated time periods. however, in this special conce...

in data envelopment analysis, the relative efficiency of a decision making unit (dmu) is defined as the ratio of the sum of its weighted outputs to the sum of its weighted inputs allowing the dmus to freely allocate weights to their inputs/outputs. however, this measure may not reflect a the true efficiency of a dmu because some of its inputs/outputs may not contribute reasonably in computing t...

Data Envelopment Analysis (DEA) is a powerful performance measurement in economic sector and operations research to assess the relative efficiency for each decision making unit (DMU). In general, there are two assumptions in DEA. Firstly, the DEA assumes that all DMUs are homogenous in their environments and secondly, the DEA is a deterministic approach which refers to not allow to noise or err...

A significant theme in data envelopment analysis (DEA) is the stability of returns to scale (RTS) classification of specific decision making unit (DMU) which is under observed production possibility set. In this study the observed DMUs are public postal operators (PPOs) in European Union member states and Serbia as a candidate country. We demonstrated a sensitivity analysis of the inefficient P...

A DEA-based stochastic estimation framework is presented to evaluate contextual variables affecting productivity. Conditions are identified under which a two-stage procedure consisting of DEA followed by regression analysis yields consistent estimators of the impact of contextual variables. Conditions are also identified under which DEA in the first stage followed by maximum likelihood estimati...

This study designs optimal systems for the centralized structures in data envelopmentanalysis (DEA). It assumes that a collection of decision making units (DMUs) with a masterdecision maker and a certain budget for them is available and introduces an optimal systemfor each DMU by maximizing their total revenue. A nmerical example is used to illustrate theproposed model.

This paper considers a scheduling problem in flexible flow shops environment with the aim of minimizing two important criteria including makespan and cumulative tardiness of jobs. Since the proposed problem is known as an Np-hard problem in literature, we have to develop a meta-heuristic to solve it. We considered general structure of Genetic Algorithm (GA) and developed a new version of that b...

In performance evaluation, it is important to identify both the efficient frontier and the critical measures. Data envelopment analysis (DEA) has been proven an effective tool for estimating the efficient frontiers, and the optimized DEA weights may be used to identify the critical measures. However, due to multiple DEA optimal weights, a unique set of critical measures may not be obtained for ...

Data envelopment analysis (DEA) is the leading technique for assessing the efficiency of decision making units (DMU) in the presence of multiple inputs and outputs. The two milestone DEA models, namely the CCR (Charnes et al., 1978) and the BCC (Banker et al., 1984) models have become standards in the literature of performance measurement. Recent applications of DEA include, among others, those...

An original data envelopment analysis (DEA) model is to evaluate each decision-making unit (DMU) with a set of most favorable weights of performance indices. The efficient DMUs obtained from the original DEA construct an efficient (bestpractice) frontier. The original DEA can be considered to identify good (efficient) performers in the most favorable scenario. For the purpose of identifying bad...