Ranking of units by positive ideal DMU with common weights

Jahanshahloo, G. R.; Lotfı, Farhad Hosseinzadeh; Khanmohammadi, M.; Kazemimanesh, M.; Rezaie, Vahideh

doi:10.1016/j.eswa.2010.04.011

Cited by 76 publications

(57 citation statements)

References 8 publications

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“…The initiation of degradation (T5%) of EPDM is found to occur at 255ºC and that of FKM at 450ºC 27 . In the case of uncompatibilized blends, the incorporation of FKM shows only a slight improvement in the initiation temperature of degradation.…”

Section: Discussion and Analysismentioning

confidence: 96%

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Efficiencies of Dipolymer Rubber Blends (EPDM\FKM) using Common Weight Data Envelopment Analysis

George

Nair

2017

Mat. Res.

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Polymer blends are generally categorized into two main classes: miscible blends that exist in a single homogeneous phase exhibiting synergistic properties and immiscible blends that have 2 or more different phases. There is also a third category of blends called technologically compatible blend, which exist in two or more different phases on micro scale, yet displays combination of properties. Ethylene-propylene-diene rubber (EPDM) and Hexa fluoropropylene-vinylidinefluoride dipolymer, Fluoroelastomer (FKM) blends with and without compatibilizer (MA-g-EPDM) were prepared by two-roll mill mixing. The aim of the work is to find out the best blend ratio and the amount of compatibilizer loading on thermal and mechanical properties by applying a novel mathematical programming technique called Data Envelopment Analysis (DEA). Using the different concentration of the ingredients used as inputs and the extent to which certain properties satisfied by the blends as outputs, a DEA model is developed. The blends which will be referred to as Decision Making Units (DMUs) were classified in terms of their efficiency. It is observed that the efficiency of all the compatibilized blends is higher than that of uncompatibilized blends. The maximum efficiency is obtained for 2.5 phr compatiblized blend.

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Section: Discussion and Analysismentioning

confidence: 96%

“…Where ε is the minimum weight restriction obtained by solving the following model 26,27 . The normalization of inputs and outputs can be performed by using the following equations:…”

Section: Common Weight Model In Deamentioning

confidence: 99%

Efficiencies of Dipolymer Rubber Blends (EPDM\FKM) using Common Weight Data Envelopment Analysis

George

Nair

2017

Mat. Res.

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“…There must be a feasible ε > 0 for each ( = 1,2, … , ) that satisfies constraints (8). And if we set ε = min {ε , ε , … , ε }, then we have a feasible ε > 0 that satisfies the following constraints:…”

Section: Theoremmentioning

confidence: 99%

“…However in our result, a full ranking of all DMUs has been got which would be more persuasive to the decision makers. By similarity with the research method in [33], we also propose a sensitivity analysis according to different weight restrictions in model (8). Figure 1(a) shows the different CCR efficiency of DMUs under different weight restrictions.…”

Section: Illustration Examplementioning

confidence: 99%

“…However the distance in their research is named the virtual gap, which is calculated by a linear program model. Another method of determining common weights is by introducing the ideal and anti-ideal DMU into the DEA model [8][9][10][11]. Ramon et al [12,13] extended their research on the cross-efficiency evaluation into the common weights DEA method based on the idea of reducing differences between profiles of weights.…”

Section: Introductionmentioning

confidence: 99%

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Determining Common Weights in Data Envelopment Analysis with Shannon’s Entropy

2014

Entropy

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Data Envelopment Analysis (DEA) is a non-parametric method for evaluating the efficiency of Decision Making Units (DMUs) with multiple inputs and outputs. In the traditional DEA models, the DMU is allowed to use its most favorable multiplier weights to maximize its efficiency. There is usually more than one efficient DMU which cannot be further discriminated. Evaluating DMUs with different multiplier weights would also be somewhat irrational in practice. The common weights DEA model is an effective method for solving these problems. In this paper, we propose a methodology combining the common weights DEA with Shannon's entropy. In our methodology, we propose a modified weight restricted DEA model for calculating non-zero optimal weights. Then these non-zero optimal weights would be aggregated to be the common weights using Shannon's entropy. Compared with the traditional models, our proposed method is more powerful in discriminating DMUs, especially when the inputs and outputs are numerous. Our proposed method also keeps in accordance with the basic DEA method considering the evaluation of the most efficient and inefficient DMUs. Numerical examples are provided to examine the validity and effectiveness of our proposed methodology.

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Green supplier selection using a common weights analysis of DEA and EOQ types of order allocation

Dobos

Vörösmarty

2020

Manage Decis Econ

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This paper offers a solution to the supplier selection problem and the problem of lot‐sizing decisions. Data Envelopment Analysis (DEA) is seldom applied to help solve multilevel supplier selection problems. The order allocation problem is reformulated as a linear programming problem in a novel way. First, a Data Envelopment Analysis/common weights model (DEA/CWA) is introduced to help define the supplier pool of capable suppliers. Selection criteria include management and green criteria. In a second phase, a nonlinear mathematical programing model is offered to determine capacity distribution among selected suppliers. A numerical example is provided to support the theoretical model.

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Ranking of units by positive ideal DMU with common weights

Cited by 76 publications

References 8 publications

Efficiencies of Dipolymer Rubber Blends (EPDM\FKM) using Common Weight Data Envelopment Analysis

Efficiencies of Dipolymer Rubber Blends (EPDM\FKM) using Common Weight Data Envelopment Analysis

Determining Common Weights in Data Envelopment Analysis with Shannon’s Entropy

Green supplier selection using a common weights analysis of DEA and EOQ types of order allocation

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