Hoda Moradi; Mozhde Rabbani; Hamid Babaei Meybodi; Mohammad Taghi Honari
Abstract
This paper presents a common set of weights (CSWs) method for multi-stage or network structured decision-making units (DMUs). The decision-making approaches proposed here consist of three stages. In the first step, a hybrid dynamic network data envelopment analysis (DNDEA) model is used to determine ...
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This paper presents a common set of weights (CSWs) method for multi-stage or network structured decision-making units (DMUs). The decision-making approaches proposed here consist of three stages. In the first step, a hybrid dynamic network data envelopment analysis (DNDEA) model is used to determine the efficiency values of the supply chain. Next, a CSW model is developed using the range-adjusted measure (RAM). In the third step, the extracted CSWs are used to compute a separate weight for each component of each DMU. the extracted CSWs are then used in the third step to calculate DMUs weights separately for each component. Then the overall efficiency is obtained by weighted averaging of the efficiency of individual components. Thus, this model evaluates the overall efficiency of a network process as well as the contribution of individual network components. The results of this study demonstrate the model’s capability to evaluate the efficiency of dynamic network structures with very high discriminatory power. In an implementation of the model in a case study, only one supplier (KARAN) earned the maximum efficiency value, and the efficiency scores of other suppliers were in the range of 0.6409-0.9983. After applying the CSWs, KARAN remained the most efficient supplier, and the efficiency scores of other suppliers moved to the range of 0.5002-0.9349. The range shifted to 0.4823-0.9921 after applying the stages weights. This weighting method should be considered an integral part of such modeling procedures, Given the enhancement observed in the results of CSW after incorporating the component weights.
Naser Ghasemi; Esmaeil Najafi; Farhad Hosseinzadeh Lotfi; Farzad Movahedi Sobhani
Abstract
Most organizations and companies have hierarchical structures, and appropriate models are required to measure the efficiency of this kind of network systems. Data envelopment analysis (DEA) is a well-known method introduced for measuring the relative efficiency of a set of decision-making units (DMUs) ...
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Most organizations and companies have hierarchical structures, and appropriate models are required to measure the efficiency of this kind of network systems. Data envelopment analysis (DEA) is a well-known method introduced for measuring the relative efficiency of a set of decision-making units (DMUs) that use multiple inputs to produce multiple outputs. Conventional DEA models usually generate misleading results while evaluating the performance of network systems. The present study aims at developing suitable models for measuring the efficiency of hierarchical structures using the centralized and non-cooperative leader-follower game models. In the proposed method, the divisional efficiencies (within an organization) and the overall efficiency of the organization are calculated. The proposed models are applied to assess the performance of 20 schools in Iran. The results of the two proposed models show that none of the schools are efficient, suggesting that these schools do not optimally utilize their resources. The application of the results of the proposed models enables managers to identify inefficient sub-units and develop strategies to improve their performance.
Nazila Adabavazeh; Mehrdad Nikbakht
Abstract
Airline industry is one of the main infrastructures for sustainable development of a country. The quality of the reverse support service will be effective in increasing the safety and health of the structures, reducing the impact of disasters and reducing costs. The aim of this study is to evaluate the ...
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Airline industry is one of the main infrastructures for sustainable development of a country. The quality of the reverse support service will be effective in increasing the safety and health of the structures, reducing the impact of disasters and reducing costs. The aim of this study is to evaluate the performance of an organization based on the main factors of reverse supply chain with the service quality approach using the Data Envelopment Analysis (DEA) model. In this research, firstly, performance indicators have been identified and then the efficiency of the 24 main factors of reverse supply chain success in the airline industry is determined by the output-oriented DEA-BCC model. The main efficient and inefficient factors are determined by EMS software. Performance measurement can be very useful for managers to allocate resources because it can provide patterns for inefficient units to achieve efficiency and performance improvements.
Mojtaba Arab Momeni; Saeed Yaghoubi; Mina Ebrahimi Arjestan
Abstract
Enterprise resources planning (ERP) systems attract many attentions from industry sector because of their ability to facilitate and integrate the enterprise operations. However, many expenses accompany the implementation of these systems and consecutive changes which doing a precise and comprehensive ...
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Enterprise resources planning (ERP) systems attract many attentions from industry sector because of their ability to facilitate and integrate the enterprise operations. However, many expenses accompany the implementation of these systems and consecutive changes which doing a precise and comprehensive performance evaluation a necessary part of this process. These evaluations should consider operational and technical aspects of ERP systems to reflect the effectiveness of them toward the organization's requirements. In this paper, a two-stage data envelopment analysis model (DEA) is presented in order to evaluate the efficiency of ERP systems in such a way that the operational and the technical aspect are evaluated in the first stage and the second stage of DEM model, respectively. Based on the obtained results the ERP systems’ functionality and customizability to the client processes are the two key features that need to be considered by the providers. Furthermore, the findings of parameter sensitivity analysis of the proposed model provide deep and managerial insights into the further usability improvements of ERP systems.
Elnaz Babazadeh; Jafar Pourmahmoud
Abstract
Super-efficiency model in the presence of negative data is a relatively neglected issue in the DEA field. The existing super-efficiency models have some shortcomings in practice. In this paper, a novel VRS radial super-efficiency DEA model based on Directional Distance Function (DDF) is proposed to provide ...
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Super-efficiency model in the presence of negative data is a relatively neglected issue in the DEA field. The existing super-efficiency models have some shortcomings in practice. In this paper, a novel VRS radial super-efficiency DEA model based on Directional Distance Function (DDF) is proposed to provide a complete ranking order of units (including efficient and inefficient ones). The proposed model is feasible no matter whether data are non-negative or not. This model shows more reliability on differentiating efficient units from inefficient ones via a new bounded super-efficiency measure. It can project each unit onto the super-efficiency frontier along a new non-negative direction and produce improved targets for inefficient units. The model overcomes the infeasibility issues occur in Nerlove–Luenberger supper-efficiency model. The proposed model conveys good properties such as monotonicity, unit invariance and translation invariance. Apart from numerical examples, an empirical study in bank sector demonstrates the superiority of the proposed model.
M. Rabbani; N. Heidari; H. Farrokhi Asl
Volume 3, Issue 2 , December 2016, , Pages 107-122
Abstract
Data envelopment analysis (DEA) is one of non-parametric methods for evaluating efficiency of each unit. Limited resources in healthcare economy is the main reason in measuring efficiency of hospitals. In this study, a bootstrap interval data envelopment analysis (BIRDEA) is proposed for measuring the ...
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Data envelopment analysis (DEA) is one of non-parametric methods for evaluating efficiency of each unit. Limited resources in healthcare economy is the main reason in measuring efficiency of hospitals. In this study, a bootstrap interval data envelopment analysis (BIRDEA) is proposed for measuring the efficiency of hospitals affiliated with the Hamedan University of Medical Sciences. The proposed method is capable to consider uncertainty and sampling errors. The inputs of this model include total number of personals, number of medical equipment, and number of operational beds. Also, outputs consist of number of inpatients, number of outpatients, number of special patients, bed-day, and bed occupancy rate. First, we estimate the efficiency by applying original DEA that does not consider any uncertainty and sampling error; then we utilize RDEA that considers uncertainty and after that we use BRDEA that consider both uncertainty and sampling error with an adaptation of bootstrapped robust data envelopment analysis and could be more reliable for efficiency estimating strategies.
Ali Yaghoubi; M. Amiri
Volume 2, Issue 2 , December 2015, , Pages 26-42
Abstract
This paper presents a new multi-objective fuzzy stochastic data envelopment analysis model (MOFS-DEA) under mean chance constraints and common weights to estimate the efficiency of decision making units for future financial periods of them. In ...
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This paper presents a new multi-objective fuzzy stochastic data envelopment analysis model (MOFS-DEA) under mean chance constraints and common weights to estimate the efficiency of decision making units for future financial periods of them. In the initial MOFS-DEA model, the outputs and inputs are characterized by random triangular fuzzy variables with normal distribution, in which data are changing sequentially. Since the initial MOFS-DEA model is a complex model, we convert it to its equivalent one-objective stochastic programming by using infinite-norm approach. To solve it, we design a new hybrid meta-heuristic algorithm by integrating Imperialist Competitive Algorithm and Monte Carlo simulation. Finally, this paper presents a real application of the proposed model and the designed hybrid algorithm for predicting the efficiency of five gas stations for the next two periods of them, with using real information which gathered from credible sources. The results will be compared with the Qin’s hybrid algorithm in terms of solution quality and runtime.