جستجوی مقالات مرتبط با کلیدواژه « data envelopment analysis » در نشریات گروه « برق »

Multi-response problems are always one of the most challenging areas of optimization. In this paper, the ROYKA hybrid method is presented to optimize multi-response problems, which is a combination of two methods of response surface and data envelopment analysis. In the ROYKA method, the efficiency surface is created and optimized, and in fact, one efficiency surface replaces several response surfaces, and the problem is changed from a multi-response mode to a single response. Therefore, in the proposed method, in addition to optimizing the response surface, the efficiency of decision-making units is also maximized. The result of this method is to determine the input and output parameters of the ideal and new decision-making unit, which also has the maximum amount of efficiency.Due to the high importance of the electricity industry and energy production, power plants, which are responsible for a very important part of electricity generation, have to increase the efficiency of their activities in order to make better use of resources. For this purpose, the proposed method of ROYKA for fourteen power plants in the country has been studied, which in addition to determining the efficiency of each power plant to determine the optimal parameters for the construction of a new power plant with maximum efficiency. Finally, in order to validate the proposed method, three other similar approaches have been used for the mentioned problem and the results show the superiority of the ROYKA method.

Many companies and manufacturing industries produce their final product after going through several stages. Normally, in addition to the desirable products, these companies also have undesirable products that are considered as non-retrieval final products. However, by adding a stage for correction and recycling, their undesirable products can be used as resources, which improves economies of scope and cost-effectiveness. The purpose of this paper is to provide models for evaluating economies of scope and cost-effectiveness in two-stage supply chain systems which undesirable products will be given to the second stage for processing and modification and will be returned to the first stage after re-modification. By examining the characteristics of the proposed models, the effect of undesirable intermediate products between stages on cost-effectiveness and economic efficiency has been measured and evaluated. The results show that the proposed models are more accurate than the conventional model and the ability to effectively distinguish between cost and economic efficiency has been significantly improved. It is also possible to study the economic efficiency in the simultaneous production of products in the proposed models.

Parameters of meta-heuristic algorithms are very effective in their performance and are usually done experimentally, which is very time-consuming. In this research, a hybrid method for selecting the optimal parameters of meta-heuristic algorithms is presented. The proposed method is a combination of data envelopment analysis methods and response surface methodology and is called DSM. In fact, this method can be used to optimize multi-objective problems and its main advantage is to create and optimize one performance response procedure instead of optimizing multiple output response procedures. In addition to optimizing parameters, it also simultaneously maximizes efficiency. In this research, the proposed DSM method has been used to adjust the parameters of the cuckoo optimization algorithm to optimize the standard and experimental Aklay and Rastrigin functions. In the hybrid DSM method, first, the efficiency value is calculated using data envelopment analysis for each set of meta-heuristic algorithm parameters, then the response procedure for performance is determined according to the meta-heuristic algorithm parameters using the response surface methodology. Finally, by optimizing the efficiency surface, the optimal values of the cuckoo algorithm parameters are obtained. In order to validate, the results of the proposed method have been compared with a similar method. The results show better performance of the hybrid algorithm in terms of solution time, number of iterations, and accuracy of the optimization function compared to other similar methods.

Growing demand for electricity and the reduction of fossil fuels have made it essential to consider alternative and renewable energy sources, such as solar and wind, to generate electricity. In this paper, by computing levelized output energy cost and levelized cost of electricity, the prices of electricity generated from the different combined cycle, wind, and photovoltaic power plants are compared. Moreover, data envelopment analysis method is used to evaluate and compare power generation technologies. The results showed that the total cost of electricity generated from fossil fuels is higher than what generated from renewable power plants and that renewable technologies have the highest compliance with sustainability criteria.

In recent years, because of the increased social concerns about the environment, the reverse logistics concept has been linked to waste management. In fact, municipal solid waste management can be considered as one of the reverse logistics issues in managing the supply chain. In this research, a model for the design of a supply chain network of reverse logistic for municipal soild waste is presented. Using the two objective of profit and biological functions, we have tried to consider two dimensions of sustainable development.The economic objective function examines the maximization of profit and the environmental objective function examines the minimization of co2 due to waste transportation and landfill storage. The proposed model is able to determine the optimal number and the location of facilities and materials flow between nodes at different levels of the network in order to achieve simultaneous economic and environmental goals. The ɛ-constraint Methods has been used to solve a multi-objective model. Also, in order to reduce the problem space and monitor the potential locations of the supply chain network facility, an effective ranking method such as data envelopment analysis is presented. The present study has been used as an applied research data from the waste management organization of Shiraz Municipality and the proposed model has been solved in GAMS software.

One of the most important issues in the supply chain is supplier selection with the aim of optimizing expenditures on uncertain demand. On the other hand, due to today's competitive environment, rising customer expectations for high quality and affordable products purchased, lead to development the long-term relationship of supply chain members including buyer and supplier. Therefore, selection of an appropriate set of efficient supplier and allocating orders to theirs, is one of the most important strategic decisions to create effective and efficient supply chain system in competitive environment that characterized by uncertainty. This study, at first, is attempted to select a set of efficient suppliers in a non-competitive environment with uncertain demand through the presenting an integrated model of multi objective programming including data envelopment analysis (DEA) and single buyer-multi vendor (supplier) coordination. Afterwards, by presenting a DEA model based on Nash bargaining game, the competitive environment among suppliers is simulated. The results of the two models shows that competitive environment has led to improved efficiency.

Several research evaluated the efficiency of electricity distribution companies by custom Data Envelopment Analysis (DEA) method, but recent researches have shown that by only reliance to the custom or optimistic DEA, we can’t certainly rank the companies according to their efficiencies, so they use pessimistic efficiency concept and integrate these two efficiencies in a mathematical function format to rank companies with high certainty and without needing to intricate calculations. In this research we want to calculate the relative efficiency by Double Frontier DEA that is more precise than the custom method. The results show that Mashhad’s electricity distribution company among the 39 studied companies has the first grade, while by using custom DEA method, ten other companies stand on the efficiency frontier.

In this paper, a multi-level benchmarking method is presented for assessing efficiency of electrical distribution companies’ expenditures. Proposed method considers mutual cooperation of capital expenditure (CAPEX) and operational expenditure (OPEX) for improving quality and decreasing energy losses. Firstly, weighted fuzzy c-means clustering (WFCM) is used by proposed method to find similar companies. Then, proposed method utilizes data envelopment analysis (DEA) and slack analysis in multi levels and through creating pseudo competition between companies, estimates companies’ efficiency scores and slacks in their expenditures. Finally, proposed IBRS is applied on Iranian distribution companies and its results are discussed.

Using more accurate approaches to evaluate the efficiency of the Iranian electricity distribution companies will help to planner to identify the best practice companies among others and enhance the quality of customer services. Usually, the data envelopment analysis (DEA) and stochastic frontier analysis (SFA) were used to assess the efficiency of decision making units. Besides the advantages of each of these methods, they have some drawbacks such as lack of proper severability or the need to shape of the production function. In this paper, stochastic nonparametric envelopment of data (StoNED) approach which combines with the two mentioned methods and has the advantages of both, are proposed to evaluate the efficiency of 39 Iranian electricity distribution companies. We defined proper input and output variables for evaluating the efficiency of each case. The proportion of underground cables in the total length of the network is taken into account as a contextual variable which is the proposed method considers a variable to better control for the heterogeneity of the firms and their operating environments. The Iranian firms based of the DEA and StoNED are evaluated and ranked. The results show, the ranking by StoNED are more reliable than the DEA. Isfahan and Kermanshah companies have the highest rank and are benchmark for others.