Selective strong and weak disposability in efficiency analysis

Mehdiloozad, Mahmood; Podinovski, Victor V.

doi:10.1016/j.ejor.2019.01.064

Cited by 34 publications

(14 citation statements)

References 42 publications

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“…However, in the general case (including some convex technologies), the nonnegativity of one-sided scale elasticities cannot be assured as, for example, in technologies with weakly disposable good outputs (Mehdiloo and Podinovski, 2019). In such cases the DRS and CRS types of RTS may require a more refined definition accounting for the possibility of the negative scale elasticity.…”

Section: Returns To Scalementioning

confidence: 99%

“…In the methodological framework of DEA, one can choose from a large number of production technologies based on different production assumptions, or axioms (Färe, Grosskopf, & Lovell, 1985). Depending on the context, such technologies may, for example, incorporate multiple component processes (Imanirad, Cook, & Zhu, 2013;Podinovski, Olesen, & Sarrico, 2018), unobserved DMUs (Thanassoulis & Allen, 1998), additional information in the form of weight restrictions and production trade-offs (Allen, Athanassopoulos, Dyson, & Thanassoulis, 1997;Podinovski, 2004c;Podinovski & Bouzdine-Chameeva, 2013), weakly disposable inputs and outputs (Mehdiloo & Podinovski, 2019), limits on the values of inputs and outputs (Cooper, Pastor, Borras, Aparicio, & Pastor, 2011) and specific network structures (Kao, 2017;Sahoo, Zhu, Tone, & Klemen, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Consistency of returns-to-scale characterizations of production frontiers with respect to model specification

Podinovski

Bouzdine‐Chameeva

2020

European Journal of Operational Research

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Section: Returns To Scalementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Consistency of returns-to-scale characterizations of production frontiers with respect to model specification

Podinovski

Bouzdine‐Chameeva

2020

European Journal of Operational Research

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“…In other words, it is essential that the variables used are capable of satisfactorily representing the efficiency of the chosen decision-making units. Mehdiloo and Podinovski (2019) discuss the problems arising from the choice of inputs or outputs that do not satisfactorily represent the proposed efficiency concept, and the mathematical influence of redundant variables that ultimately overlap. Considering the importance of choosing variables that satisfactorily represent the concept of efficiency presented by Farrell (1957), the selection of the variables was based on the theoretical framework, and it was subsequently validated by conducting semi-structured interviews with industry experts in Brazil.…”

Section: Quantitative Stagementioning

confidence: 99%

Environmental, field and impurity factors to increase the agricultural performance of Brazilian and Australian sugarcane mills

Carlucci

Lemos

Salgado

et al. 2021

Clean Techn Environ Policy

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Graphic abstract This study aims to identify explanatory factors to increase the agricultural performance of Brazilian and Australian sugarcane mills. The relevance of Brazil and Australia for the sugar industry motivated the development this study based on the most important factors in both countries responsible for increasing the efficiency in sugarcane production. Thus, this study is designed to assess the hypothesis that there are a few explanatory variables that are deeply responsible for the agricultural efficiency in the sugar-energy sector. As a specific objective, it proposes a DEA (Data Envelopment Analysis) model that seeks to optimize the production of Total Recoverable Sugar (TRS) by planted area, and simultaneously, minimizes mineral and vegetable impurities. The sample consists of 82 observations from 32 sugarcane mills. An agricultural efficiency study was performed using the two-stage DEA, in which the evaluated mills according to the level of efficiency in the proposed model. Then, a Multiple Linear Regression Analysis was performed to identify the variables with the greatest influence on the performance of the mills in terms of efficiency. The results revealed six relevant variables for increasing the agricultural performance in the production of sugarcane: rainfall (mm weekly), chopped cane delivery (%), delivery time (h), borer (%), air humidity (%), and rods in raw wine (× 10 5 /mL). Finally, semi-structured interviews with Brazilian and Australian experts in the sugar-energy sector allowed the identification of five other relevant complementary factors that were unavailable in the database: genetic variety, agricultural cultivation activities, edaphoclimatic factors, renewal of sugarcane fields and irrigation system. The results of this study were grouped into the dimensions of environment, yield, and impurities, providing quantification and better understanding of the identified explanatory factors and the agricultural performance in terms of production efficiency, offering fundamental information that enables managers to make decisions and prioritize the aspects that contribute more significantly to the increase in agricultural productivity of the planted area.

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“…The class of polyhedral technologies including the standard CRS and VRS technologies of Charnes et al (1978) and Banker et al (1984), the non-increasing and non-decreasing returns-to-scale technologies (Färe et al 1983, Seiford andThrall 1990), and their extensions by production trade-offs which are dual terms to weight restrictions (Podinovski 2004b). Further examples include the hybrid returns-to-scale technology based on the idea of selective proportionality between certain subsets of inputs and outputs (Podinovski 2004a), technologies with multiple component processes, including those with joint or shared inputs and outputs (Cherchye et al 2013, Podinovski et al 2018, and models based on (selective) weak disposability of inputs and outputs (Mehdiloo and Podinovski 2019). Finally, the underlying technologies of various network DEA models (Färe and Grosskopf 2000) are also polyhedral.…”

Section: Introductionmentioning

confidence: 99%

Optimal solutions of multiplier DEA models

Podinovski

Bouzdine‐Chameeva

2021

J Prod Anal

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Conventional models of data envelopment analysis (DEA) are based on the constant and variable returns-to-scale production technologies. Any optimal input and output weights of the multiplier DEA models based on these technologies are interpreted as being the most favorable for the decision making unit (DMU) under the assessment when the latter is benchmarked against the set of all observed DMUs. In this paper we consider a very large class of DEA models based on arbitrary polyhedral technologies, which includes almost all known convex DEA models. We highlight the fact that the conventional interpretation of the optimal input and output weights in such models is generally incorrect, which raises a question about the meaning of multiplier models. We address this question and prove that the optimal solutions of such models show the DMU under the assessment in the best light in comparison to the entire technology, but not necessarily in comparison to the set of observed DMUs. This result allows a clear and meaningful interpretation of the optimal solutions of multiplier models, including known models with a complex constraint structure whose interpretation has been problematic and left unaddressed in the existing literature.

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Selective strong and weak disposability in efficiency analysis

Cited by 34 publications

References 42 publications

Consistency of returns-to-scale characterizations of production frontiers with respect to model specification

Consistency of returns-to-scale characterizations of production frontiers with respect to model specification

Environmental, field and impurity factors to increase the agricultural performance of Brazilian and Australian sugarcane mills

Optimal solutions of multiplier DEA models

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