Determining a common set of weights in a DEA problem using a separation vector

Chiang, C. I.; Hwang, Ming‐Jiu; Liu, Y. H.

doi:10.1016/j.mcm.2011.06.002

Cited by 39 publications

(25 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Development of their method for ranking of efficient candidates in voting systems may also produce interesting results. • Chiang et al [2] used a separation method for locating a CSW in DEA. To analyze the methods of finding the CSW, they solved a particular form of a multiple objectives fractional linear programming problem (MOFP) and then based on a special characteristic utilized an auxiliary vector to convert the MOFP to a single objective linear programming to obtain a CSW for calculating the DMU's efficiency ratio.…”

Section: Discussionmentioning

confidence: 99%

“…The last two constraints of model (2), called the assurance region constraints, ensure that the votes of the higher place has a greater importance that of the lower place. Cook and Kress [3] investigated the choice of function d (., ε) has significant impact on the winner.…”

mentioning

confidence: 99%

“…, t), then the preference score of candidate r in the place i will be t j=1 v r i j , which is exactly the number of votes in place i that candidate r receives. In this case, the preference score of candidate r is equal to the one in model (2) in which all voters are in one category. Thus, the value of z r in (4) indicates the real score of each candidate.…”

mentioning

confidence: 99%

“…Remark 1 Ebrahimnejad [6] extended a new voting model by considering the priority of the voters based on model (2). But, the choice of d (., ε) has the influence on the resulting overall ranking.…”

mentioning

confidence: 99%

See 3 more Smart Citations

A novel approach for discriminating efficient candidates by classifying voters in the preferential voting framework

Ebrahimnejad

2015

Japan J. Indust. Appl. Math.

View full text Add to dashboard Cite

A key issue in the preferential voting framework is how voters express their preferences on a set of candidates. In the existing voting systems, each voter selects a subset of candidates and ranks them from most to least preferred. The obtained preference score of each candidate is the weighted sum of votes receives in different places. However, most of the existing voting systems assume vote of all voters have equal importance and there is no preference among them. In this paper, a new voting system is introduced to overcome this drawback. In the proposed voting system is assumed that voters are classified into several categories in which the vote of voters in a higher category is more important than the ones in a lower category. Then, an existing ranking method is generalized to rank multiple efficient candidates based on comparing the least preference scores for each efficient candidate with the best and the least preference scores measured in the same range. The proposed method is illustrated with two application examples which prove to be persuasive and acceptable to the election systems.

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

A novel approach for discriminating efficient candidates by classifying voters in the preferential voting framework

Ebrahimnejad

2015

Japan J. Indust. Appl. Math.

View full text Add to dashboard Cite

show abstract

“…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. Some other techniques have also been introduced into the DEA method for determining common weights, such as goral programming [14], regression analysis [15], robust optimization [16] and so on [17][18][19][20]. Applications of the common weights DEA models can be found in economy evaluation [21], technology selection [22], resource allocation [23], and so on.…”

Section: Introductionmentioning

confidence: 99%

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

2014

Entropy

View full text Add to dashboard Cite

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.

show abstract

Measuring Olympics achievements based on a parallel DEA approach

Lei

Xie

et al. 2014

Ann Oper Res

View full text Add to dashboard Cite

Determining a common set of weights in a DEA problem using a separation vector

Cited by 39 publications

References 7 publications

A novel approach for discriminating efficient candidates by classifying voters in the preferential voting framework

A novel approach for discriminating efficient candidates by classifying voters in the preferential voting framework

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

Measuring Olympics achievements based on a parallel DEA approach

Contact Info

Product

Resources

About