Data Envelopment Analysis (DEA) is a mathematical programming technique for evaluating the relative efficiency of a set of Decision Making Units (DMUs) and can also be utilized for setting Target. Target setting is one of the important subjects since according to its results efficiency can be increased. An important issue to be currently discussed, is to set Target while considering share data. These data for each individual indicate the share of the unit, which takes part in an activity, from the whole amount which is a predefined constant. It is obvious that the sum of units’ share is equal to the entire amount. Thus, any changes in the magnitude of these data has to be dependent on the changes in data of other units. In this paper a two-stage procedure is developed to find benchmark units where share data exist. The fact that all DMUs are jointly projected onto the new efficient frontier and simplicity, are the significant features of the proposed method. With a numerical example we demonstrate how this method works.

In this article, we , rst survey the relationship between Lozano and Villa's centralized resource allocation (CRA) and Mar-Molinero's simpli, ed models. Then, we show how to obtain an opti-mal solution to the CRA model via the simpli, ed model. Moreover, we propose a novel model by simplifying phase II of Lozano and Villa's model. This proposed model determines a peer e, cient unit for each decision making unit (DMU) through some simple computations. The proposed simpli, ed models help us to clarify some ambiguities in the CRA method and propose some solutions to solve them. Considering the importance of Target setting in the management, we formulate an-other new model such that by preserving the aim of the CRA model, also projects DMUs onto e, cient frontier according to desirable Target as much as possible. The proposed model is illustrated by using a data set of earlier literature.

In this paper, the focus is on additive models with interval data. An additive model can be converted to a multi-objective linear problem if information about preferences of the consumption of inputs and the production of outputs are taken into account. Here in this study, data are not exact and are of interval kind. Moreover, the most preferred solutions with available information by interval additive models are sought. It has also been shown that if additional information is available, an axial solution can be applied.Also, the most preferred Target settings will be computed too. In this study twenty bank branches in Iran are evaluated, and Target settings and efficiency are compared with the original case and significant decisions are made.

Data Envelopment Analysis (DEA) is used as an important tool for Target setting. In this paper the methods studied in the literature have been collected and compared.

Basic models of Data Envelopment Analysis are intrinsically preference-free, in the sense that they consider all inputs and outputs and also all decision making units of the same importance. Although this property is beneficial in many ways, it has some drawbacks simultaneously, as the decision makers’ preferences are not taken into account in the process of evaluating units. To overcome this drawback many researchers have developed several techniques for incorporating the preferences into the evaluation model. One of the underlying approaches is value efficiency analysis, which evaluates decision making units by comparing them with the most preferred unit. The most preferred unit is a unit which satisfies the decision maker most. On the other hand, the issue of benchmarking is an important aspect in data envelopment analysis, as it enables the analyst to choose a Target for each inefficient unit. The Target unit for each unit is located on the efficient frontier and determines the path of improvement for that inefficient unit. In this paper, the issue of Target setting based on the concept of value efficiency is investigated. We aim to develop a Target setting model which is able to determine Target units that are not only efficient but also value efficient, as well. Moreover, the Targets are determined based on closest distance from the evaluated unit. Some properties of the model are also discussed. Finally, we perform the proposed model on a real data set of 42 Spanish Universities.

One of the main challenges in Irans auto industry is goal setting in various functional domains.Managing costs of supply chains requires recognition of cost drivers and mathematical modeling for Target setting. Realization of the main elements of supply chains and setting logical relations among variables and parameters in the form of a mathematical model are of the main purposes of this research paper. The main question is how it is possible to bring conict goals of a supply chain together in a mathematical model with the help of goal programming. The relations among 10 au (1) 1to part suppliers, 1 warehouse, 3 auto manufacturing plants and 3 agencies are investigated in this study.After collecting data of model parameters, model is solved through application of LINGO software and the following results are revealed. The positive goal deviation of the first objective is equal to 2500 million Rials. The negative goal deviation of the second objective is equal to 1750 million Rials.The positive goal deviation of the third objective is 1250 million Rials.

Data envelopment analysis as an e ective approach to evaluate the relative e ciency of decisionmaking units, recently applied for developing production plans in terms of resource allocation and Target setting in many production and operation systems. Most of these systems are of network structures where the outputs from a given stage are intermediate measures which form the inputs for the next stage. This paper proposes a new k-stage DEA based multi objective linear programming model for Target setting in the serial network structures, considering undesirable outputs. Since the proposed model takes the size of under evaluation units into account when developing the inputs/outputs arrangements, the new idea ensures the possibility of the results. An empirical example of a production system is used to illustrate the applicability of the proposed model. The results show that based on the new plan, all of the e ciency scores are improved.