فهرست مقالات Network DEA

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  1 - An Inverse Dynamic FDH Approach to Estimate Outputs
  F. Asadi S. Kordrostami AR. Amirteimoori M. Bazrafshan
  In many situations, the performance and the changes of outputs related to dynamic systems should be estimated while the convexity property is relaxed. Accordingly, first, a dynamic free disposal hull (FDH) model is proposed in this paper to address the efficiency of pro چکیده کامل

  In many situations, the performance and the changes of outputs related to dynamic systems should be estimated while the convexity property is relaxed. Accordingly, first, a dynamic free disposal hull (FDH) model is proposed in this paper to address the efficiency of processes in multiple period of time while the convexity assumption is unsatisfied. Also, two problems, including a mixed integer linear programming and a linear programming model are provided to compute the dynamic FDH model that is a mixed integer non-linear programming problem. Then the changes of multi-period outputs are dealt with for changes of inputs related to several periods using the proposed inverse dynamic FDH model while the efficiency levels are preserved. A case study of gas industry is, moreover, presented to demonstrate the introduced models. The results show the proposed technique is useful to analyze the performance and to estimate outputs in dynamic processes without including convexity. پرونده مقاله
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  2 - The Design of Inverse Network DEA Model for Measuring the Bullwhip Effect in Supply Chains with Uncertain Demands
  Sajjad Aslani Khiavi Simin Skandari
  10.22094/joie.2020.1885077.1703
  Two different bullwhip effects with equal scores may have different sensitivities and production patterns. As a result, the difference between these two seemingly equal scores has been ignored in previous methods (such as frequency response and moving average). So, the چکیده کامل

  Two different bullwhip effects with equal scores may have different sensitivities and production patterns. As a result, the difference between these two seemingly equal scores has been ignored in previous methods (such as frequency response and moving average). So, the present study constructs a model of Inverse Network Data Envelopment Analysis, to introduce the relative and interval scores of the bullwhip effect magnitude, when a series of uncertain demands are made in a specific time interval. In the first stage of the proposed network, the uncertain demands and the forecasted uncertain data are regarded respectively as the model’s inputs and outputs. These output data constitute the intermediate variables and consequently the inputs of the second stage of the study model. In the second stage, after considering the ordering policies, the uncertain orders are sent. Due to utilizing both the optimistic and pessimistic perspectives, the study methodology includes an interval value for measuring the bullwhip effect with relative attitude. In the optimistic perspective, the analyzed decision making unit has the optimal status in comparison with other decision making units. In the pessimistic perspective, the analyzed decision making unit has the worst status in comparison with other decision making units. The results show that time is an unfair factor in the size of the bullwhip effect. The impact of uncertainties on the bullwhip effect in the demand forecasting stage is greater than the ordering stage. According to the research findings, cross-sectional planning is possible at different times according to different conditions. Therefore, using the results of the research, a fair score of the bullwhip effect can be obtained by considering all perspectives. پرونده مقاله
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  3 - Interval Efficiency Assessment in Network Structure Based on Cross –Efficiency
  nasim roudabr seyed esmaeil najafi
  As we know, in evaluating of DMUs some of them might be efficient, so ranking of them have a high significant. One of the ranking methods is cross-efficiency. Cross efficiency evaluation in data envelopment analysis (DEA) is a commonly used skill for ranking decision ma چکیده کامل

  As we know, in evaluating of DMUs some of them might be efficient, so ranking of them have a high significant. One of the ranking methods is cross-efficiency. Cross efficiency evaluation in data envelopment analysis (DEA) is a commonly used skill for ranking decision making units (DMUs). Since, many studies ignore the intra-organizational communication and consider DMUs as a black box. For significant of this subject, we applied cross-efficiency for network DMUs. However, In view of the fact that precise input and output data may not always be available in real world due to the existence of uncertainty, we have developed the model with interval data. the existing classical interval DEA method is not able to rank the DMUs, but can only classify them as efficient or inefficient , so this paper improve that. The proposed method can be used for each network that includes DMUs with two stages in production process. However, this paper is the first study that examined cross efficiency of DMUs in structure framework with interval data. the new approach enables us to ranking of first stage for n DMU and second stages of them. DMUs with the best rank can be used as benchmark for improving efficiency of other DMUs. Finally, We present Illustrate example with two steps for proposed model that can be develop for more than two steps. پرونده مقاله
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  4 - Evaluation of the Performance in Dynamic Network Data Envelopment Analysis with Undesirable Outputs
  محمد نجاری الموتی رضا کاظمی متین محسن خون سیاوش زهره مقدس
  Data Envelopment Analysis (DEA) is a mathematical technique to assess the performance of Decision Making Units (DMUs) with similar inputs and outputs. The traditional DEA models disregard the internal structure of units and have a “black box” view. Thus, to چکیده کامل

  Data Envelopment Analysis (DEA) is a mathematical technique to assess the performance of Decision Making Units (DMUs) with similar inputs and outputs. The traditional DEA models disregard the internal structure of units and have a “black box” view. Thus, to evaluate the structures with more than one stage, the network DEA (NDEA) models expanded. On the other hand, the dynamic optimization models have been presented to eliminate the limitations of static models in optimization. In the article, for the first time, a systematic approach is used to present a dynamic NDEA with constant inputs and undesirable outputs. First, we used an axiomatic approach in DEA with undesirable output and presented an NDEA model with undesirable output. Then, we extended the proposed approach and presented a dynamic NDEA with undesirable output and a constant input. Afterward, we applied this model to evaluate hospitals’ performance in an experimental study to estimate the efficiency of their dynamic network. پرونده مقاله