Abstract This paper deals with an inverse data envelopment analysis (DEA) based on the non-radial slacks-based model in presence of uncertainty employing both integer and continuous interval data. To this matter, suitable technology formulation for DEA are proposed using arithmetic partial orders numbers. The is discussed from following question: if output $$DMU_o$$ <mml:math xmlns:mml="http://...

Data envelopment analysis (DEA) measures the efficiency score of a set homogeneous decision-making units (DMUs) based on observed input and output. Considering input-oriented, inverse DEA models find required level for producing given amount production in current level. This article proposes new form model considering income (for planning) budget finance budgeting) constraints. In contrast with...

DEA models treat the DMU as a “black box.” Inputs enter and outputs exit, with no consideration of the intervening steps. Consequently, it is di(cult, if not impossible, to provide individual DMU managers with speci;c information regarding the sources of ine(ciency within their DMUs. We show how to use DEA to look inside the DMU, allowing greater insight as to the sources of organizational ine(...

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). Recently network DEA models been developed to examine the efficiency of DMUs with internal structures. The internal network structures range from a simple two-stage process to a complex system where multiple divisions are linked together with intermediate measures. In general, there ar...

Data envelopment analysis (DEA) is an effective method to evaluate the relative efficiency of decision-making units (DMUs). In one hand, the DEA models need accurate inputs and outputs data. On the other hand, in many situations, inputs and outputs are volatile and complex so that they are difficult to measure in an accurate way. The conflict leads to the researches of uncertain DEA models. Thi...

The present study addresses the following question: if among a group of decision making units, the decision maker is required to increase inputs and outputs to a particular unit in which the DMU, with respect to other DMUs, maintains or improves its current efficiencylevel, how much should the inputs and outputs of the DMU increase? This question is considered as a problem of inverse data envel...

Because of the unreasonable and unnecessary weight restriction assumption, a barely noticeable deficiency in pseudo-inefficiency occurs at times when applying a CCR model. The CCR was the first model designed for Data Envelopment Analysis (DEA); it remains its most popular model. To detect this type of unobvious deficiency, this study compared the following: the efficiency score and optimal wei...

As increasing disbursements (such as costs and fees) risk portfolio leverage risk) may affect managed fund performance, analysts go beyond risk-adjusted return measures for performance appraisal. A methodology that assesses in a multidimensional framework is data envelopment analysis (DEA). variant of DEA inverse DEA. In this paper, applied to determine output (investment income benefit payment...

this paper proposes a new approach for determining efficient dmus in dea models using inverse optimi-zation and without solving any lps. it is shown that how a two-phase algorithm can be applied to detect effi-cient dmus. it is important to compare computational performance of solving the simultaneous linear equa-tions with that of the lp, when computational issues and complexity analysis are a...

Quantifying cartilage contact stress is paramount to understanding hip osteoarthritis. Discrete element analysis (DEA) is a computationally efficient method to estimate cartilage contact stresses. Previous applications of DEA have underestimated cartilage stresses and yielded unrealistic contact patterns because they assumed constant cartilage thickness and/or concentric joint geometry. The stu...