Optimal weights in DEA models with weight restrictions

Podinovski, Victor V.

doi:10.1016/j.ejor.2016.04.035

Cited by 60 publications

(30 citation statements)

References 32 publications

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“…In this case the optimal weight is not in the best comparison. (Podinovski, 2016) Hence, we should approach to separate the innovation input and output is possible because the first identified and the smooth surface are fitted. (Sreedevi, Vimala, Rameswari, Sateesh, & Professor, 2016) But we should make a powerful link by the DEA fuzzy method to find the weight and affection of inputs to output as a strong bridge.…”

Section: Analysis By Dea Methodsmentioning

confidence: 99%

The Science and Technology Parks (STPs) Evaluation Model Approach to Eco-Innovation Key Indicator

Yami¹,

Gao²,

Gao³

2018

IBR

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Science and Technology Park (STP) is one of the most important innovation policies to develop the regional economy. To manage the STPs successfully, a standard evaluation system as a reference is needed. However, there is no consensus about the definition of successful STPs due to their different goals and regions. Hence, it is necessary to establish a reference framework to evaluate the success of different STPs and it is essential to assess their main goals as the competitive advantage by a set of innovation indicators. This study developed a research model to evaluate the competitiveness of STPs by analyzing the impact of innovation subjective externalities based on the Global Innovation Index (GII) and approach to the eco-innovation key indicator. This STP evaluation model is adopted and tested by two different fuzzy analyzing and examines the survey forms and questionnaires that have been filled by some STP experts and as a case study all the evidence has been gathered and analyzed from “Caohejing Hi-Tech Park” in Shanghai and the results evaluated the competitive advantage via innovation policies and performances and the rating rank contents some innovation main dimensions, key indicators and factors and also the important result as eco-innovation development and diffusion.

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Section: Analysis By Dea Methodsmentioning

confidence: 99%

The Science and Technology Parks (STPs) Evaluation Model Approach to Eco-Innovation Key Indicator

Yami¹,

Gao²,

Gao³

2018

IBR

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“…Figure 1 displays the correlations between the outputs, with darker colours pointing at higher correlations. This matrix reveals subsets of outputs highly correlated with each other, such as outputs 23 to 31, where correlations are above 0.5, except for corr(23, 27) = 0.37 and corr (27,29)…”

Section: Numerical Sectionmentioning

confidence: 99%

Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach

Benítez-Peña¹,

Bogetoft²,

Morales³

2020

Omega

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This paper proposes an integrative approach to feature (input and output) selection in Data Envelopment Analysis (DEA). The DEA model is enriched with zero-one decision variables modelling the selection of features, yielding a Mixed Integer Linear Programming formulation. This single-model approach can handle different objective functions as well as constraints to incorporate desirable properties from the real-world application. Our approach is illustrated on the benchmarking of electricity Distribution System Operators (DSOs). The numerical results highlight the advantages of our single-model approach provide to the user, in terms of making the choice of the number of features, as well as modeling their costs and their nature.

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“…The results of Podinovski and Bouzdine-Chameeva (2013) imply that, if program (8) is infeasible, the constraints u, v ≥ ϵ1 generate free production of output vectors in the expanded technology. The meaning of optimal input and output weights in DEA models with weight restrictions has recently been explored by Podinovski (2016).…”

Section: The Single-stage Solution Approachmentioning

confidence: 99%

Solving DEA models in a single optimization stage: Can the non-Archimedean infinitesimal be replaced by a small finite epsilon?

Podinovski

Bouzdine‐Chameeva

2017

European Journal of Operational Research

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Single-stage DEA models aim to assess the input or output radial efficiency of a decision making unit and potential mix inefficiency in a single optimization stage. This is achieved by incorporating the sum of input and output slacks, multiplied by a small (theoretically nonArchimedean infinitesimal) value epsilon in the envelopment model or, equivalently, by using this value as the lower bound on the input and output weights in the dual multiplier model. When this approach is used, it is common practice to select a very small value for epsilon. This is based on the expectation that, for a sufficiently small epsilon, the radial efficiency and optimal slacks obtained by solving the single-stage model should be approximately equal to their true values obtained by the two separate optimization stages. However, as well-known, selecting a small epsilon may lead to significant computational inaccuracies. In this paper we prove that there exists a threshold value, referred to as the effective bound, such that, if epsilon is smaller than this bound, the solution to the single-stage program is not approximate but precise (exactly the same as in the two-stage approach), provided there are no computational errors.

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Optimal weights in DEA models with weight restrictions

Cited by 60 publications

References 32 publications

The Science and Technology Parks (STPs) Evaluation Model Approach to Eco-Innovation Key Indicator

The Science and Technology Parks (STPs) Evaluation Model Approach to Eco-Innovation Key Indicator

Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach

Solving DEA models in a single optimization stage: Can the non-Archimedean infinitesimal be replaced by a small finite epsilon?

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