A Modified Russell Measure for Estimating Efficiency Changes in the Presence of the Undesirable Outputs and Stochastic Data

Hosseini, Seyedeh Sara; Saen, Reza Farzipoor; Matin, Reza Kazemi

doi:10.24200/sci.2021.58051.5538

Cited by 1 publication

(1 citation statement)

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“…For the first time stochastic data in DEA discussed by Sengupta (1982) by considering stochastic inputs and outputs in measuring efficiency. Over the last decade some scholars have addressed stochastic data in DEA such as Azadi and Farzipoor Saen (2011), Kaffash and Marra (2017), Tavassoli et al (2020), Hosseini et al (2021), and.…”

Section: Dea and Network Deamentioning

confidence: 99%

RETRACTED ARTICLE: Evaluating sustainably resilient supply chains: a stochastic double frontier analytic model considering Netzero

et al. 2022

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In era of reglobalization, sustainably resilient supply chains (SCs) are imperative in corporations to improve performance and meet stockholders’ expectations. However, sustainably resilient SCs could not be effective if are not assessed by using advanced frameworks, systems, and models. As such, developing a novel network data envelopment model (DEA) to appraise sustainably resilient SCs is our purpose in this article. To do so, we present a new double-frontier methodology to provide optimistic and pessimistic efficiency measures in network structures. Moreover, ideas of outputs weak disposability, chance-constrained programming, and discrete dominance are incorporated in a unified framework of modelling efficient and inefficient production technologies. The new network DEA model also can address dissimilar types of data, including undesirable and integer-valued and ratio outputs, stochastic intermediate products, and integer-valued inputs in a unified framework. Furthermore, an aggregated Farrell type efficiency measure is developed which allows to provide the complete ranking of units so that each decision-making unit (DMU) has its own rank in both overall and divisional point of view. We show the unique features of our developed model using a real case study in paint industry to evaluate the efficiency and reducing carbon dioxide (CO2) emissions. The results show that how well the proposed models can evaluate the sustainability and resilience of supply chains in the presence of uncertainty and with dissimilar types of data.

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