•This paper proposes a neutral cross efficiency model for basic two-stage systems.•Proposed model can obtain more realistic weight scheme than basic two stage systems.•Proposed model can fully rank ...the units in overall (system) and sub-stages.•The system efficiency is the product of the sub-stages efficiencies.
Cross efficiency evaluation in data envelopment analysis (DEA) is an effective tool for ranking the performance of decision-making units (DMUs). Numerous cross efficiency evaluations have been proposed with different secondary goals using both peer-evaluation and self-evaluation. The neutral cross efficiency evaluation is an important secondary goal in the classical black-boxes DEA models; that is, the internal processes of the DMUs are often ignored in the efficiency evaluation. This study extends the idea of neutral cross evaluation to measure the efficiency of the basic two stage network systems and proposes a new neutral cross efficiency model. The proposed model is able to rank DMUs in sub-stages and decompose the cross efficiency measure of the system into the product of those of the stages. The results from two real-world examples show that the neutral cross efficiency model proposed in this paper can increase the discriminating power of a two-stage system and their sub-stages and can obtain more realistic weight scheme than basic two stage DEA model.
•The proposed model completely eliminates the risk of selecting inappropriate epsilon.•The proposed model excludes non-Archimedean epsilon.•The solution of the proposed model can be carried out in ...one-step.•The proposed model can also calculate a common set of weights to evaluate the performance of a unit.
Data envelopment analysis is a very effective mathematical instrument in assessing the performance of decision-making units. In most real cases, the decision-maker need to identify a single most efficient unit. Several approaches were proposed for this necessity in the literature using data envelopment analysis. This study, based on the two steps model suggested by Toloo and Salahi (2018), proposes a new model without epsilon to choose the most efficient unit. The proposed model has fewer constraints than their model and is solved by a one-step linear programming model without epsilon. The proposed model determines exactly one DMU as the most efficient one and other decision-making units have efficiency scores strictly less than one. A simulation study was designed to test the proposed model in terms of some criteria such as correlation. In addition, the examples of real cases whose real rank is known and frequently used in literature of the most efficient unit were preferred for the validity of the proposed model. The results illustrated that the discrimination power problem was experienced in the previous models whereas no such problem was observed in the new proposed model for the same real cases.
Data envelopment analysis (DEA) is a very effective management tool in assessing the performance of a set of decision making units (DMUs). In the efficiency evaluation using classic single stage DEA ...models, the internal processes of the DMUs are often neglected. In most real-world problems, it may be more realistic to evaluate the efficiency evaluation in two-stage production systems. In some cases: however, the decision-maker must need to identify the most efficient single unit. Numerous methods have been introduced to find the most efficient unit in single stage systems whereas no methods have been proposed for this aim in two stage production systems. Therefore, a new model based on mixed-integer programming was proposed to determine the most efficient DMU in two-stage systems and sub-stages in this study. The most important innovation of the suggested approach is that the most efficient DMUs of both stages can be found separately using only one model. Numerical examples for real world problems and a simulation study were provided for the validity of the proposed model.
Electric distribution companies have a significant role for both households and industries. Benchmarking of the electric distribution companies in the energy sector has become a subject that is ...studied widely nowadays due to the effect of privatization policies for developing countries. Since there are multiple production stages regarding the generation and supply procedures of electric power, Network DEA technique is used. Directional Distance Function is also integrated into Network DEA technique. Electric distribution companies are organizations that are aiming at maximizing profit while minimizing the expenses. The main problem is how the profit idea can be integrated into the evaluation process. The aim of the proposed model is to evaluate profit efficiency of electric distribution companies while taking into account expansion cost for additional energy supply. This two stage approach is applied to Turkish electric distribution companies. Results are presented based on radial and profit efficiency measures. The proposed model is demonstrates realistic results by considering the expenses and incomes of distribution companies.
•A Network DEA model for performance measurement of Turkish electricity companies.•Integration of expansion cost.•Electricity distribution companies are ranked based on profit efficiency measure.•Real life application of the model to 20 electricity distribution companies.
Data envelopment analysis (DEA) has been a very popular method for measuring and benchmarking relative efficiency of peer decision making units (DMUs) with multiple input and outputs. Beside of its ...popularity, DEA has some drawbacks such as unrealistic input–output weights and lack of discrimination among efficient DMUs. In this study, two new models based on a multi-criteria data envelopment analysis (MCDEA) are developed to moderate the homogeneity of weights distribution by using goal programming (GP). These goal programming data envelopment analysis models, GPDEA-CCR and GPDEA-BCC, also improve the discrimination power of DEA.
The transport sector is one of the most important sectors with the potential to reduce greenhouse gas emissions, according to Türkiye's 2053 net zero target. Electrification is the most important ...part of this goal, is expected to become basic strategy for transport. Thus, this paper aims to analyze how the transport sector could take part in terms of transport policies to achieve the target. For that purpose, Transport Sector Energy Model based on Türkiye Energy Model is designed, the transport sector is analyzed, and the results are presented under two scenarios: The Stated Policies Scenario and the Net Zero Emissions Scenario. According to the analysis of model results, the development of sink areas and timing of low carbon policies such as CO2 standards, technology constraints, and behavioral changes of transport are crucial for the net zero target emission of Türkiye.
•The Turkish transportation sector is evaluated in view of net zero emission target.•Advances in sink areas may directly affect reaching the net zero emissions target.•The timing of policies like CO2 standards and technology restrictions may be crucial.•It seems that policy times alone will not be enough in the period 2025–2055.•Changing transportation modes helps to reduce emissions, notably in freight transport.
► Data classification is one of the fundamental problems in many decision-making tasks. Artificial neural networks (ANN) have popularity in the data classification problems. One of the important ...issues on the neural networks is training of the networks. Backpropagation algorithm is the most widely used search technique for training neural networks. Backpropagation algorithm has negative properties such as being captured in the local solutions and having low classification performance in some cases. In order to prevent these disadvantages, researchers have proposed many alternatives. Genetic algorithms are efficient alternatives for training of the neural networks. It is known that the comparison of the approaches is as important as proposing a new classification approach. In this study, the training of the ANNs for the classification problems is examined by the backpropagation, binary-coded and real-coded genetic algorithm. In order to compare these training algorithms, 10 different real-world and large-scale simulation datasets covering the different network architectures is used. The results based on real-data and simulation show that classification success of artificial neural network model trained with real-coded genetic algorithm is better than other training methods.
Artificial neural networks (ANN) have a wide ranging usage area in the data classification problems. Backpropagation algorithm is classical technique used in the training of the artificial neural networks. Since this algorithm has many disadvantages, the training of the neural networks has been implemented with the binary and real-coded genetic algorithms. These algorithms can be used for the solutions of the classification problems. The real-coded genetic algorithm has been compared with other training methods in the few works. It is known that the comparison of the approaches is as important as proposing a new classification approach. For this reason, in this study, a large-scale comparison of performances of the neural network training methods is examined on the data classification datasets. The experimental comparison contains different real classification data taken from the literature and a simulation study. A comparative analysis on the real data sets and simulation data shows that the real-coded genetic algorithm may offer efficient alternative to traditional training methods for the classification problem.
The generalized gamma distribution (GGD) is a popular distribution because it is extremely flexible. Due to the density function structure of GGD, estimating the parameters of the GGD family by ...statistical point estimation techniques is a complicated task. In other words, for the parameter estimation, the maximizing likelihood function of GGD is a problematic case. Hence, alternative approaches can be used to obtain estimators of GGD parameters. This paper proposes an alternative parameter estimation method for GGD by using the heuristic optimization approaches such as Genetic Algorithms (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). A comparison between different modern heuristic optimization methods applied to maximize the likelihood function for parameter estimation is presented in this paper. The paper also investigates both the performance of heuristic methods and estimation of GGD parameters. Simulations show that heuristic approaches provide quite accurate estimates. In most of the cases, DE has better performance than other heuristics in terms of bias values of parameter estimations. Besides, the usefulness of an alternative parameter estimation method for GGD using the heuristic optimization approach is illustrated with a real data set.
This paper explores the structural and operational dimensions of the efficiencies of airports. The two-stage procedure is suggested to assess the efficiencies of airports in this study. In the ...first-stage, Classification and Regression Tree, which is one of the machine-learning approaches used to divide the airports into homogeneous and thus comparable sub-groups. In the second stage, the bootstrap data envelopment analysis approach obtains more precise structural and operational efficiency scores. To illustrate the proposed framework use, we applied it to a real case associated with Turkish airports. The results demonstrate that this framework presents a more comprehensive assessment of airport performance rather than conventional data envelopment analysis models. Moreover, it provides to show the deficiencies of the structural and operational management of airports. The findings can help anywhere airport authorities as well as Turkish airport authorities.
•The efficiency of airports should be assessed as structural and operational separately.•New objectives like incentives should be proposed to improve weak efficiencies.•Homogeneous and comparable groups should be obtained using cluster analysis methods.
In this paper we introduce a goal programming formulation for the multi-group classification problem. Although a great number of mathematical programming models for two-group classification problems ...have been proposed in the literature, there are few mathematical programming models for multi-group classification problems. Newly proposed multi-group mathematical programming model is compared with other conventional multi-group methods by using different real data sets taken from the literature and simulation data. A comparative analysis on the real data sets and simulation data shows that our goal programming formulation may suggest efficient alternative to traditional statistical methods and mathematical programming formulations for the multi-group classification problem.
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