جستجوی مقالات مرتبط با کلیدواژه "data envelopment analysis" در نشریات گروه "علوم پایه"

Deprivation and elimination of deprivation from different regions of the country to achieve sustainable development is one of the important issues in Iran. Therefore, the country's budget structure needs to be reformed. The purpose of this research is to evaluate the special view of the Islamic Consultative Assembly towards deprived areas in the amendment of the plan for eliminating deprivation, Note 14 of the budget law of the year 1401, using Data Envelopment Analysis (DEA) method. Since the decision-making units are the provinces of Iran, we have used the output-oriented CCR model to determine the efficiency of design modification, and then we have ranked it with the MAJ model. We have also determined important indicators in the allocation of credit to eliminate deprivation in provinces by using the AHP approach. Therefore, it is suggested that the note of this table should be deleted based on the text presented in a double-urgency plan agreed upon by main factions of the Parliament, and its credit should be distributed according to valid deprivation indicators. As well as this, we suggest that the requirements of each region should be met based on the latest statistics and relevant information.

Performance measurement is always considered one of the most important tasks of managers. Hence, management knowledge is measurement knowledge and if we cannot measure something, we certainly cannot control it and consequently we cannot manage it. In this paper, we examine data envelopment analysis models for improving inefficient units. In this study, 20 bank branches in Tehran were selected and mathematical models were presented for estimating inputs with interval data. The findings of this research highlight the importance of integrating advanced analytical tools like DEA into management practices. By quantifying inefficiencies and offering clear pathways for improvement, DEA empowers managers to make data-driven decisions that enhance overall performance. This approach is particularly valuable in competitive environments, such as the banking sector, where efficiency and service quality directly impact customer satisfaction and profitability.

One of the common concerns of investors is determining the suitable field for investment. Due to the attractiveness of online sales in various fields such as clothing, newcomers and even existing companies tend usually to sell online. In this research, the rank of the suitability of an investment for online sales in different fields of clothing in Shiraz City was determined using the data envelopment analysis method. In the beginning, we form an expert team. Also, we recognized ten fields of clothing as investment alternatives for online sales (DMUs). Then, we defined suitable inputs and outputs by reviewing the literature and obtaining the opinions of expert team members. Also, we determine an epsilon-based input-oriented BCC model as a suitable DEA model for DMU ranking. Then, we obtained the input and output values from the expert team members and considered the average values as the inputs and outputs of the DEA models. Formulating and solving epsilon-based input-oriented BCC models showed that three DMUs were inefficient, and the other seven DMUs were efficient. Therefore, the rank of these three DMUs was determined. Next, to determine the rank of the other seven DMUs, we formed and solved the Andersen-Peterson epsilon-based input-oriented BCC models. The results of solving the DEA models showed that the fields of "Designing, producing, and selling of wedding dresses", "Designing, producing, and selling suits and formal dresses", and "Designing, producing, or selling local clothing" have the first to third ranks, respectively.

In many production systems, we can do acquisition and merge operations process to increase productivity. For this purpose, we can use the inverse data envelopment analysis (DEA) approach. In many cases, in addition to producing desirable outputs, we also have the simultaneous production of undesirable outputs. It is important to use a suitable approach in the acquisition and merge operations process. In this paper, we present a new model based on the directed distance function. The new model provides a new unit or a pre-determined target efficiency level by merging two decision-making units (DMUs). Based on this model, level for desirable and undesirable outputs is determined for the newly created unit. In the following, we will show the provided approach with a numerical example and apply it for real world data.

Productivity, a crucial aspect of economics, refers to the efficient use of resources to maximize output. In today’s world, enhancing productivity is vital for economic growth and competitiveness in global markets. Improvements in productivity lead to cost reductions, increased profitability, and better quality of products and services. This study analyzes changes in total factor productivity by examining data from 20 manufacturing companies in the construction sector listed on the stock exchange. It aligns with the country’s Fifth Development Plan and uses the Malmquist Index as the primary tool for measuring productivity. The Malmquist Index assesses technical and scale efficiency to identify productivity changes over time. The investigation covers the period from 2010 to 2013, reflecting various economic and market conditions. The findings can help managers and policymakers pinpoint strengths and weaknesses in production processes, offering strategies to enhance productivity and efficiency in the construction industry. Additionally, these results provide valuable insights for researchers and practitioners interested in productivity and efficiency. Given the importance of the topic, this article contributes to understanding the factors affecting productivity in manufacturing and aids in developing strategies to improve the country’s economic performance.

Finding units with the most productive scale size (MPSS) is very important. The use of MPSS in ranking is thus the main idea in this paper. We propose an algorithm in DEA that ranks all extreme and non-extreme efficient DMUs in a number of steps. In this method, units with the most productive scale size are identified in each step and are then ranked. We finally show the application of the method using a numerical example.

In order to improve the performance of inefficient decision-making units (DMU), it is important to find an efficient target. This target determines the amount of changes in inputs and outputs; by applying these changes, efficiency is achieved. The usual models in DEA always considered the problem of improving inefficient DMU and in this regard, they present a target as an efficient DMU. But in action, for some DMUs achieving that target in one step is difficult and even impossible. For this reason, finding intermediate target is very important. In this way, instead of an inefficient DMU becoming efficient in one step, this work is done in several steps and in this case the improvement is obtained gradually. In this paper, the efficiency of DMUs is evaluated using the CCR model, and then a sequence of intermediate targets is provided for each inefficient DMU. Moving in this direction will reduce the inefficiency of these DMUs.

The efficiency score of the decision making units (DMUs) depends on the input and output values. The efficiency score of the DMU in the presence of undesirable outputs is greater or equal to the efficiency score of this DMU in the absence of undesirable outputs. To face this problem, we present a new ratio based data envelopment analysis (DEA-R) model to measure the effects of undesirable outputs on the efficiency of production units. In this regard, we first introduce the counterpart (hypothetical) units corresponding to the original DMUs. These units use the same amount of input to produce the same desirable outputs as the original DMUs, but produce a small amount of undesirable outputs compared to the original units. In the following, we use non-radial DEA-R models based on slacks corresponding to all the ratios of input components to desirable output and the ratios of undesirable output to desirable output to measure efficiency in the presence of undesirable outputs. Also, let's use the efficiency ratio of the main units to their corresponding counterpart units as a reduction factor to show the impact of undesirable outputs. To show the validity of the proposed approach, we evaluate the performance of thirty paper mills and present the results.

In traditional Data Envelopment Analysis (DEA) approaches, inputs and outputs are usually considered as exact and real values. The relative efficiency of the Decision Making Units (DMUs) is evaluated and it is known that the factors are inputs and/or outputs. However, there are some conditions under which the efficiency of DMUs should be calculated while the data are integer and ambiguous. Therefore, various integer DEA models have been proposed to determine the performance of DMUs when integer data and fuzzy factors are available. In addition, there are cases where the efficiency of DMUs should be determined when integer data and flexible factors are available. Therefore, some integer DEA methods have been proposed to calculate the performance of DMUs and specify the role of flexible measures when some of the data are integer and flexible factors are available. However, there are some situations where there are integer data, fuzzy integer measures and flexible factors. Therefore, this paper sheds light on the nature of the model to determine the efficiency of DMUs when there are integer inputs and/or outputs, flexible factors and fuzzy integer measures, and determines the role of factors with uncertain inputs or outputs. In fact, slacks are addressed and a slack-based efficiency measure (SBM) is defined to compute the performance of DMUs in the presence of flexible factors, integer data and fuzzy integer measures. The proposed approaches are demonstrated and illustrated using an example.

Traditional cost models ignore the internal structure of decision-making units (DMUs), so, may produce ambiguous outcomes and provide a biased assessment. In this paper, we evaluate the performance of the units by considering their internal structures. We proposed a new cost Malmquist index for measuring the cost productivity change of the units with bi-level structures. The bi-level structure is a special case of hierarchical structures with two levels, where the leader unit is positioned at the upper level and followers are located at the lower level. The overall system of bi-level units tries to use inputs and produce outputs in a cost-efficient way. However, each subunit performs according to its goals and limited resources. This research tries to develop a bi-level cost model that is suitable for measuring the cost efficiency of bi-level units. Based on this model, a new cost Malmquist index (CMI) is suggested to evaluate the productivity changes of bi-level units. This index presents a new aspect of CMI and provides the productivity changes of units by considering the impact of the leader's and the subunits' performance. In addition, similar to the traditional CMI, it decomposes into various components, such as cost efficiency changes and cost technological changes. The developed CMI is applied to a real-world case study to evaluate eight management regions which all together manage 198 branches. The results show that the proposed CMI provides a more meaningful evaluation of DMUs compared to the conventional CMI.

This research aims to develop data envelopment analysis (DEA) for predicting the supply chain sustainability of the concrete industry using stochastic variables. In the first phase, the mines and companies that organized the four supply chains of the Guilan concrete industry based on competitive elements were identified. After that, based on economic, social, and environmental indexes, the sustainability of the supply chains was chosen using the Fuzzy Delphi model and input and output of mines and concrete companies, which include controllable, uncontrollable, and undesirable inputs and outputs. Finally, data envelopment analysis was used to measure the supply chain sustainability of the concrete industry employing crisp data and a linear model. The errors were entered into the model with a stochastic element based on the probability of the errors. In other words, according to stochastic data envelopment analysis, all input and output data were considered randomly. The output data shows that none of the four supply chains of the Guilan concrete industry is sustainable.

  In the traditional data envelopment analysis (DEA) models, the role of measures from input and output aspects is known. However, in many cases, we face a situation where some measures can play the role of input or output. The role of these measures is determined as input or output with the aim of maximizing the efficiency of the decision making unit (DMU) under evaluation. In this paper, we present a novel inverse DEA model to classify these inputs and outputs. We determine the new level of inputs and outputs and flexible measures by choosing the target efficiency for the DMUs. In this regard, the new model may choose flexible measures as input or output, but the main goal is to reach the target efficiency level. In the following, we will illustrate the presented approach with a simple numerical example. Finally, a numerical real example propose in the banking industry in Indonesia to clarify and demonstrate the suggested approach. We also bring the results of the models.

Data Envelopment Analysis (DEA) is a Nonparametric method for measuring and does not need to have and calculate the production function, which is often difficult to calculate.
In this article, we evaluate the units under investigation in terms of cost and production efficiency in several time periods and the progress(Improvements) or regression of each unit.for this purpose,we use the method based on solving linear programing models using Malmquist productivity index .Data envelopment analysis is a non-parametric method for measuring the performance of decision making units.Finally, by designing and solving a numerical example, we emphasize and test the applicability of the material presented in this article.

The primary models in data envelopment analysis (DEA), consider the inputs and outputs of the decision-making units (DMUs) as non-negative. However, in the real world, we face many cases where the data is negative. In this paper, we investigate the inverse DEA models to estimate the optimal level of inputs and outputs of DMUs based on target efficiency scores. We also assume that some input and output components are negative. In this way, we propose three different models in variable returns to scale (VRS) to determine optimal levels. In order to solve each model, we determine the counterpart DMU corresponding to the DMUs under evaluation. This DMU is obtain based on the additive model, and then we get the level of the target and the observed outputs corresponding to the DMU under evaluation to determine which of these three models to use to measure the efficiency of the DMU under evaluation. We apply the proposed approach with a numerical example and consider it to measure the optimal levels of inputs and outputs of bank branches. Also we propose the results of paper.

This Evaluation of fuzzy networks with imprecise data is crucial. In this article, we propose fuzzy two-stage network models based on the structure of central resource allocation models. Firstly, we obtain the target for the fuzzy decision-making units in the two-stage network by using central resource allocation models, with a maximum of one two-phase model in each stage of the network. Then, we determine the overall target for the network. The probability function approach is used in the two-stage fuzzy network models to rephrase the proposed models and find the target. In conclusion, we calculate the target for Iranian airlines using fuzzy data and the proposed model.

In this paper, we evaluate the performance of decision-making units (DMUs) in semi-additive production technology in the presence of production trade-offs. We introduce the semi-additive production technology. The semi-additive technology is based on all the DMUs observed and the set of aggregated DMUs corresponding to these DMUs in data envelopment analysis (DEA). We obtain production trade-offs on input and output components in the production process in semi-additive technology. We present a single-stage model to measure efficiency in the presence of production trade-offs in semi-additive production technology. This model also identifies inefficiencies in all input and output components. We show an application of the presented model in the banking industry and at the end we bring the results of the paper.

This study aims to provide a comprehensive evaluation of the technical efficiency and scale of 15 suppliers of a production unit from 2020 to 2022. The research utilizes Data Envelopment Analysis (DEA) to analyze two scale assumptions that are generally employed: constant returns to scale (CRS), and variable returns to scale (VRS). The variables for the study were selected based on indicator availability, representation principles, and expert opinions. Investment, nonoperating expense cost and operating costs (including raw material costs, wages, and overhead costs) were considered as inputs, while net sales and return on investment were regarded as outputs. The results indicate that only two suppliers were operating at the optimal scale, and the scale efficiency of the supply chain displayed an increasing dispersion over the mentioned period. However, the net technical efficiency of the supply chain demonstrated an increasing concentration, suggesting an overall reduction in the gap between suppliers and an improvement in pure technical efficiency within the manufacturing unit's supply chain. This study provides valuable insights into the differences between suppliers from a macro perspective and offers guidance for manufacturing units looking to expand their supply chain.

Evaluating the performance of organizations can provide managers with useful information about the status of the organization compared to other organizations so that managers can take a step towards the growth and excellence of the organization. Obviously, the number of indicators and their amount affect the performance evaluation of organizations. So, by collecting the exact values of the indicators, an accurate and accurate performance evaluation will be provided to managers of organizations. In this article, we intend to evaluate the companies investing in the stock exchange. Since in the table of indices related to these companies published by the Iran Stock Exchange Organization there are indices whose values have been lost for any reason (not available - heterogeneous index), it is necessary to use envelopment analysis models. We used data (DEA) in the presence of heterogeneous indicators. We have used the model of Cook et al.'s (2013) article to evaluate companies. For the conceptual use of research, we have described and implemented their method step by step. Lastly, we have analyzed the results.

The efficiency evaluation of organizational units provides managers with a perspective on the current state of the organization and solutions for their improvement. One of the methods of organizational evaluation is to determine the organization's minimum cost or cost efficiency. Cost efficiency in practice can be calculated when the input prices are available. In traditional models of cost efficiency, input and output data are crisp. However, there are situations where input and/or output may be imprecise. For such cases, experts are invited to model their opinion. Then uncertainty theory can be applied which is introduced by Liu as a mathematical branch rationally dealing with belief degrees. In this paper, a model is proposed to estimate the cost of decision-making units in the uncertain environment, where inputs and outputs are uncertain but the input prices are crisp. Several theorems are presented to discuss some features of the introduced model. When the data has a linear distribution, the cost efficiencies of the decision-making units are calculated. Also, the model is implemented on two numerical examples. The obtained results are compared with previous results. Finally, in the presence of input prices, a different cost efficiency score for the decision-making units is obtained. The proposed model helps decision-makers to improve their performance by using experts' opinions.

‎Data envelopment analysis (DEA) is a methodology widely used for evaluating the relative performance of portfolios under a mean–variance framework‎. ‎However‎, ‎there has been little discussion of whether nonlinear models best suit this purpose‎. ‎Moreover‎, ‎when using DEA linear models‎, ‎the portfolio efficiency obtained is not comparable to those on the efficient portfolio frontier‎. ‎This is because a separable piecewise linear boundary usually below the efficient frontier is considered the efficient frontier‎, ‎so the model does not fully explore the possibility of portfolio benchmarks‎. ‎In this paper‎, ‎and with use of the dual-Lagrangian function‎, ‎we propose a linear model under a mean–variance framework to evaluate better the performance of portfolios relative to those on the efficient frontier‎.