Review of ranking methods in the data envelopment analysis context

Adler, Nicole; Friedman, Lea; Sinuany‐Stern, Zilla

doi:10.1016/s0377-2217(02)00068-1

Cited by 713 publications

(318 citation statements)

References 56 publications

Supporting

Mentioning

302

Contrasting

Unclassified

Order By: Relevance

“…Besides RAM, there are several promising methods to rank DMUs. See the reviews by Adler et al (2002) and Markovits-Somogyi (2011) regarding the methods mentioned hereafter. Other ranking methods missing from both reviews exist, such as the 'global efficiencies' (GLE) approach by Despotis (2002) which, like RAM, uses a common weighting scheme across all DMUs.…”

Section: Regional Differencesmentioning

confidence: 99%

See 1 more Smart Citation

An approach to holistically assess (dairy) farm eco-efficiency by combining Life Cycle Analysis with Data Envelopment Analysis models and methodologies

Soteriades

Faverdin

Moreau

et al. 2016

Animal

View full text Add to dashboard Cite

Eco-efficiency is a useful guide to dairy farm sustainability analysis aimed at increasing output (physical or value added) and minimizing environmental impacts (EIs). Widely used partial eco-efficiency ratios (EIs per some functional unit, e.g. kg milk) can be problematic because (i) substitution possibilities between EIs are ignored, (ii) multiple ratios can complicate decision making and (iii) EIs are not usually associated with just the functional unit in the ratio's denominator. The objective of this study was to demonstrate a 'global' eco-efficiency modelling framework dealing with issues (i) to (iii) by combining Life Cycle Analysis (LCA) data and the multiple-input, multiple-output production efficiency method Data Envelopment Analysis (DEA). With DEA each dairy farm's outputs and LCA-derived EIs are aggregated into a single, relative, bounded, dimensionless eco-efficiency score, thus overcoming issues (i) to (iii). A novelty of this study is that a model providing a number of additional desirable properties was employed, known as the Range Adjusted Measure (RAM) of inefficiency. These properties altogether make RAM advantageous over other DEA models and are as follows. First, RAM is able to simultaneously minimize EIs and maximize outputs. Second, it indicates which EIs and/or outputs contribute the most to a farm's eco-inefficiency. Third it can be used to rank farms in terms of eco-efficiency scores. Thus, non-parametric rank tests can be employed to test for significant differences in terms of eco-efficiency score ranks between different farm groups. An additional DEA methodology was employed to 'correct' the farms' eco-efficiency scores for inefficiencies attributed to managerial factors. By removing managerial inefficiencies it was possible to detect differences in eco-efficiency between farms solely attributed to uncontrollable factors such as region. Such analysis is lacking in previous dairy studies combining LCA with DEA. RAM and the 'corrective' methodology were demonstrated with LCA data from French specialized dairy farms grouped by region (West France, Continental France) and feeding strategy (regardless of region). Mean eco-efficiency score ranks were significantly higher for farms with <10% and 10% to 30% maize than farms with >30% maize in the total forage area before correcting for managerial inefficiencies. Mean eco-efficiency score ranks were higher for West than Continental farms, but significantly higher only after correcting for managerial inefficiencies. These results helped identify the eco-efficiency potential of each region and feeding strategy and could therefore aid advisors and policy makers at farm or region/sector level. The proposed framework helped better measure and understand (dairy) farm eco-efficiency, both within and between different farm groups.

show abstract

Section: Regional Differencesmentioning

confidence: 99%

“…Note that RAM can only rank inefficient DMUs. In fact, ranking efficient DMUs was not desirable here because rankings can differ between methods (see Adler et al, 2002), possibly affecting the results of the non-parametric rank statistics.…”

Section: Regional Differencesmentioning

confidence: 99%

An approach to holistically assess (dairy) farm eco-efficiency by combining Life Cycle Analysis with Data Envelopment Analysis models and methodologies

Soteriades

Faverdin

Moreau

et al. 2016

Animal

View full text Add to dashboard Cite

show abstract

“…They discussed about causes and improvements in solution methodology to minimize those difficulties. Adler et al (2002) extensively reviewed the ranking methods of DEA. Diaz et al (2004) tested DEA for 35 irrigation districts of Andalusia, Southern Spain for three inputs, namely irrigated surface area in hectares, labour in annual working units, and total volume of water applied to an irrigation district in hectare m 3 and output being agricultural production in Euros.…”

Section: Introductionmentioning

confidence: 99%

Ranking Irrigation Planning Alternatives Using Data Envelopment Analysis

Raju

Kumar

2006

Water Resour Manage

View full text Add to dashboard Cite

Abstract. Application of Data Envelopment Analysis (DEA) as a Multicriterion Decision Making(MCDM) methodology is tested for Sri Ram Sagar Project, Andhra Pradesh, India to select the suitable irrigation planning alternative. Three different criterion functions of DEA, namely minimizing deviation variable D j (Min D j ), minimizing maximum deviation (Minmax), and minimizing the sum of deviations D j (Minsum) are applied for the same DEA constraint set. These criterion functions are evaluated under the framework of Multi Objective Linear Programming (MOLP). Highest efficiency rated irrigation planning alternative is chosen to be the best for each of the above criterion functions. The results are compared with those obtained by discrete MCDM methods, PROMETHEE and EXPROM. It is found that ranks obtained by DEA are reasonably close to those obtained by the above mentioned MCDM methods, PROMETHEE and EXPROM.

show abstract

“…Data envelopment analysis (DEA) is a powerful method, especially because the different weight factors are determined by the method itself. However, the consequence is that the method is only applicable if the number of systems is larger than the number of the aspects [33,34] and that is not the case in our current topic.…”

Section: Methods Of Assessmentmentioning

confidence: 92%

Reverse electrodialysis: evaluation of suitable electrode systems

Veerman

Saakes²,

Metz³

et al. 2010

J Appl Electrochem

238

188

View full text Add to dashboard Cite

Reverse electrodialysis (RED) is a method for directly extracting electrical energy from salinity gradients, especially from sea and river water. For the commercial implementation of RED, the electrode system is a key component. In this paper, novel electrode systems for RED were compared with existing systems on safety, health, environment, technical feasibility and economics. Systems with inert DSA-type electrodes and a NaCl-HCl supporting electrolyte with the reversible Fe 2? /Fe 3? redox couple or the [Fe(CN) 6 ] 4-/Fe(CN) 6 ] 3-couple achieved the highest ranking. Improvements of the electrode system are also discussed like the use of special stable metal electrodes, graphite electrodes, other reversible redox couples, capacitive electrodes and electrolytes with carbon particles.

show abstract

Review of ranking methods in the data envelopment analysis context

Cited by 713 publications

References 56 publications

An approach to holistically assess (dairy) farm eco-efficiency by combining Life Cycle Analysis with Data Envelopment Analysis models and methodologies

An approach to holistically assess (dairy) farm eco-efficiency by combining Life Cycle Analysis with Data Envelopment Analysis models and methodologies

Ranking Irrigation Planning Alternatives Using Data Envelopment Analysis

Reverse electrodialysis: evaluation of suitable electrode systems

Contact Info

Product

Resources

About