Robust worst-practice interval DEA with non-discretionary factors

Arabmaldar, Aliasghar; Mensah, Emmanuel Kwasi; Toloo, Mehdi

doi:10.1016/j.eswa.2021.115256

Cited by 16 publications

(3 citation statements)

References 52 publications

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“…Thus one may combine it with our proposed ASBM models in the presence of undesirable outputs. Moreover, the idea of uncontrollable inputs [33], non-discretionary factors [24] and uncertainty in dataset [1,23,28], have also pervasive applications, so including them in the proposed models would be an absorbing future research directions.…”

Section: Discussionmentioning

confidence: 99%

A two-stage structure with undesirable outputs: slacks-based and additive slacks-based measures DEA models

et al. 2022

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The slacks-based measure (SBM) and additive SBM (ASBM) models are two widely used DEA models acting based on inputs and outputs slacks and giving efficiency scores between zero and unity. In this paper, we use both models with the application of the weak disposability axiom for outputs to evaluate efficiency in a two-stage structure in the presence of undesirable outputs. In the external evaluation, the SBM model is reformulated as a linear program and the ASBM model is reformulated as a second-order cone program (SOCP) that is a convex programming problem. In the internal evaluation, the SBM model for a specific choice of weights is linearized while the ASBM model is presented as an SOCP for arbitrary choice of weights. Finally, the proposed models are applied on a real dataset for which efficiency comparison and Pearson correlation coefficients analysis show advantages of the ASBM model to the SBM model.

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Section: Discussionmentioning

confidence: 99%

A two-stage structure with undesirable outputs: slacks-based and additive slacks-based measures DEA models

et al. 2022

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“…Together with the widespread use of the RO approach in diverse fields, several studies have been carried out to tackle the problems of uncertainty in DEA (see e.g. Shokouhi et al 2010 , 2014 ; Sadjadi et al 2011 ; Omrani 2013 ; Lu 2015 ; Arabmaldar et al 2017 ; Tavana et al 2021 ; Arabmaldar et al 2021 ; Hatami-Marbini and Arabmaldar 2021 ;). This strand of DEA models is generally based on the three RO approaches proposed by Mulvey et al ( 1995 ), Ben-Tal and Nemirovski ( 2000 ) and Bertsimas and Sim ( 2004 ).…”

Section: Uncertain Data In the Malmquist Indexmentioning

confidence: 99%

Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry

2022

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This paper aims to contribute to the contemporary and imperative research on the performance and productivity growth of the oil industry. Among cutting edge methods, frontier analysis is a successful approach that has been widely used to assess the efficiency and productivity of entities with multiple resources and multiple outputs. This study first develops a unique framework based upon data envelopment analysis (DEA) to measure efficiency and productivity in the way that it tackles the uncertainty in data and undesirable outputs and, in turn, provides useful information to decision-makers. An adaptive robust optimisation is utilised to combat uncertain data whose distributions are unknown and consider the nexus between the level of conservatism and decision-makers’ risk preference. The key advantage of the proposed robust DEA approach is that the results remain relatively unchanged when uncertain conditions exist in the problem. An empirical study on the oil refinery is presented in situations of data uncertainty along with considering CO 2 emissions as the undesirable output to conduct environmental efficiency and productivity analysis of the 25 countries over the period 2000–2018. The empirical results obtained from the proposed approach give some imperative implications. First, results show that the price of robustness does not affect identically for varying technologies when assessing productivity in a global oil market, and the USA oil industry is observed as the highest productivity growth in all cases confirming its efforts for the rapid rise in oil extraction and production at low costs. There may be practical lessons for other nations to learn from the USA oil industry to improve productivity. Findings also support a considerable regress during the 2008 Global Financial Crisis in the oil industry compared to the rest of the periods in question, and due to monetary and fiscal stimulus, there is a sharp productivity growth from 2009 to 2011. The other implication that can be drawn is that the GDP growth rate and technology innovation can more effectively improve the productivity of the oil industry across the globe. Supplementary Information The online version contains supplementary material available at 10.1007/s00291-022-00683-y.

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“…Sensitivity analysis is also used to determine how sensitive the decisionmaking units' (DMUs) solution values and efficiency scores are to numerical observations. By changing the DMUs' reference set, the proposed new model tests the robustness of DEA efficiency scores (Arabmaldar et al, 2021). For this purpose, in this study, the DEA method has been used.…”

Section: Data Envelopment Analysis (Dea)mentioning

confidence: 99%

Environmental impact assessment and efficiency of cotton: the case of Northeast Iran

Mahdei

Esfahani²,

Lebailly

et al. 2022

Environ Dev Sustain

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Cotton is one of the important crops that play an important role in creating a livelihood for rural people in many parts of Iran. Cotton production necessitates a large amount of resources (e.g., fossil energy and agrochemicals, all of which have the potential to damage the environment in various ways). The purpose of the current study was to evaluate the environmental effects of cotton production in the South Khorasan Province of Iran. For this purpose, Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA) techniques have been applied to investigate the environmental impacts of cotton production. LCA is a practical method to evaluate the environment on the product flow, in which all aspects of the product life cycle are examined by a comprehensive approach. Furthermore, combining the LCA method with other managerial strategies such as DEA could allow researchers to provide decision-makers with more practical and interpretable data. The findings of the efficiency test showed that the average technical efficiency, pure technical efficiency, and scale efficiency were 0.81, 0.92, and 0.87, respectively. Respiratory inorganics (i.e., respiratory effects resulting from winter smog caused by emissions of dust, sulfur, and nitrogen oxides to air) posed the greatest environmental burden in cotton production, followed by non-renewable energy, carcinogens, and global warming. In addition, the highest effects were on human health, and then, on resources and climate change. Energy, on-system pollution, and waste played a crucial role in the environmental impacts of cotton processing. This study suggests improving farmers' knowledge toward the optimum application of chemical fertilizers, or their substitution with green fertilizers, which reduces the environmental effect of growing cotton in the area.

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Robust worst-practice interval DEA with non-discretionary factors

Cited by 16 publications

References 52 publications

A two-stage structure with undesirable outputs: slacks-based and additive slacks-based measures DEA models

A two-stage structure with undesirable outputs: slacks-based and additive slacks-based measures DEA models

Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry

Environmental impact assessment and efficiency of cotton: the case of Northeast Iran

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