Park (2010) [8] presented a method to obtain the upper bound on efficiency in imprecise data envelopment analysis (IDEA) in which the envelopment model with imprecise data had been used. In this paper, we consider the dual model, the multiplier model, which involves the non-Archimedean element ε . Then, we define a model to determine the upper bound of ε . An assurance interval for the non-Arch...

in the portfolio selection problem, the manager considers several objectives simultaneously such as the rate of return, the liquidity and the risk of portfolios. these objectives are conflicting and incommensurable. moreover, the objectives can be imprecise. generally, the portfolio manager seeks the best combination of the stocks that meets his investment objectives. the imprecise goal program...

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 mathematical programming-based approach for evaluates the relative efficiency of a set of DMUs (Decision Making Units). The relative efficiency of a DMU is the result of comparing the inputs and outputs of the DMU and those of other DMUs in the PPS (Production Possibility Set). Also, in Data Envelopment Analysis various models have been developed in order to...

The purpose of this paper is to evaluate the revenue efficiency in the fuzzy network data envelopment analysis‎. ‎Precision measurements in real-world data are not practically possible‎, ‎so assuming that data is crisp in solving problems is not a valid assumption‎. ‎One way to deal with imprecise data is fuzzy data‎. ‎In this paper‎, ‎linear ranking functions are used to transform the full fuz...

The main goal of this paper is to propose a new approach for efficiency measurement and ranking of stocks. Data envelopment analysis (DEA) is one of the popular and applicable techniques that can be used to reach this goal. However, there are always concerns about negative data and uncertainty in financial markets. Since the classical DEA models cannot deal with negative and imprecise values, i...

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision making units (DMUs) on the basis of multiple inputs and outputs. The context-dependent DEA is introduced to measure the relative attractiveness of a particular DMU when compared to others. In real-world situation, because of incomplete or non-obtainable information, the data (Input and ...

This paper presents a novel approach to achieving the goals of data envelopment analysis in the process of reconstruction and integration of decision-making units by using multiple objective linear programming. In this regard, first, we review inverse data envelopment analysis models for data reconstruction and integration. We present a model with multi-objective linear programming structure in...