Global and local scale characteristics in convex and nonconvex nonparametric technologies: A first empirical exploration

Cesaroni, Giovanni; Kerstens, Kristiaan; Woestyne, Ignace Van de

doi:10.1016/j.ejor.2016.10.030

Cited by 33 publications

(8 citation statements)

References 51 publications

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“…It is also worth considering the incorporation of selective strong and weak disposability assumptions in nonconvex production technologies, including free disposal hull of Deprins, Simar, and Tulkens (1984). Developing the notion of RTS for such technologies presents a further conceptual challenge (Cesaroni, Kerstens, & Van de Woestyne, 2017). From the theoretical perspective, questions remain open regarding the dual interpretation of the selective disposability assumptions in the sense of Shephard's lemma (Färe, & Primont, 1995) and, in the context of undesirable outputs, their relationship to the notion of null-jointness (see, e.g., Färe and Grosskopf, 2004).…”

Section: Discussionmentioning

confidence: 99%

Selective strong and weak disposability in efficiency analysis

Mehdiloozad

Podinovski

2019

European Journal of Operational Research

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Section: Discussionmentioning

confidence: 99%

Selective strong and weak disposability in efficiency analysis

Mehdiloozad

Podinovski

2019

European Journal of Operational Research

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“…The first point at which this ray intersects the shaded area is corresponding to α CB . In Figure 2, this point is (2,4), and so α CB = 2.…”

Section: Stepmentioning

confidence: 99%

Closed form of the response function in FDH technologies: Theory, computation and application

Mostafaee

Soleimani-damaneh

2020

RAIRO-Oper. Res.

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Response function (RF), which gives the value of maximum feasible outputs in response to changing the inputs, has a crucial role in performance analysis and scale elasticity measurement. In this paper, a polynomial-time algorithm is provided which is able to obtain the closed form of the RF under (nonconvex) FDH productions technologies. Finite convergence of the presented algorithm is proved; and it is established that the algorithm is polynomial-time from a complexity standpoint. Moreover, an application of the proposed procedure with real-world data accompanying some experiment-based computational discussions are given.

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“…In order to calculate the overall and stage efficiency scores based on dissimilar RTS assumptions, some closed form models are applied. Finally, some recent FDH models studies which have been proposed by many scholars can be addressed in literature [39][40][41][42][43].…”

Section: Two-stage Fdh Model In Production Technologiesmentioning

confidence: 99%

A Novel Machine Learning Approach Combined with Optimization Models for Eco-efficiency Evaluation

et al. 2020

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Machine learning approaches have been developed rapidly and also they have been involved in many academic findings and discoveries. Additionally, they are widely assessed in numerous industries such as cement companies. Cement companies in developing countries, despite many profits such as valuable mines, face many challenges. Optimization, as a key part of machine learning, has attracted more attention. The main purpose of this paper is to combine a novel Data Envelopment Analysis (DEA) approach in optimization at the first step to find the Decision-Making Unit (DMU) with innovative clustering algorithms in machine learning at the second step introduce the model and algorithm with higher accuracy. At the optimization section with converting two-stage to a simple standard single-stage model, 24 cement companies from five developing countries over 2014–2019 are compared. Window-DEA analysis is used since it leads to increase judgment on the consequences, mainly when applied to small samples followed by allowing year-by-year comparisons of the results. Applying window analysis can be beneficial for managers to expand their comparison and evaluation. To find the most accurate model CCR (Charnes, Cooper and Rhodes model), BBC (Banker, Charnes and Cooper model) and Free Disposal Hull (FDH) DEA model for measuring the efficiency of decision processes are used. FDH model allows the free disposability to construct the production possibility set. At the machine learning section, a novel three-layers data mining filtering pre-processes proposed by expert judgment for clustering algorithms to increase the accuracy and to eliminate unrelated attributes and data. Finally, the most efficient company, best performance model and the most accurate algorithm are introduced. The results indicate that the 22nd company has the highest efficiency score with an efficiency score of 1 for all years. FDH model has the highest efficiency scores during all periods compared with other suggested models. K-means algorithm receives the highest accuracy in all three suggested filtering layers. The BCC and CCR models have the second and third places, respectively. The hierarchical clustering and density-based clustering algorithms have the second and third places, correspondingly.

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Global and local scale characteristics in convex and nonconvex nonparametric technologies: A first empirical exploration

Cited by 33 publications

References 51 publications

Selective strong and weak disposability in efficiency analysis

Selective strong and weak disposability in efficiency analysis

Closed form of the response function in FDH technologies: Theory, computation and application

A Novel Machine Learning Approach Combined with Optimization Models for Eco-efficiency Evaluation

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