Energy Saving and Management of the Industrial Process Based on An Improved DEA Cross-model

Geng, Zhiqiang; Bai, Ju; Zhu, Qunxiong; Xu, Yuan; Han, Yangming

doi:10.1109/ddcls.2018.8515973

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…Moreover, because the interrelationship among DMUs is neglected, DEA has a major lack of ability to distinguish between those DMUs at all (Zuo & Guan, 2017). In another word, if the number of indicators is high compared to the number of DMUs, the evaluation operation will face a problem and the model will present an efficiency value of 1 for most organizations mistakenly (Geng et al, 2018), therefore, the DMUs number must be minimally twice higher than the total indicators number, i.e., sum of inputs and outputs (Nourani et al, 2018). Generally, incorporating qualitative, subjective, and intuitive indicators in DEA is not possible.…”

Section: Introductionmentioning

confidence: 99%

DEVELOPING AN INTEGRATED MODEL FOR EVALUATING R&D ORGANIZATIONS’ PERFORMANCE: COMBINATION OF DEA-ANP

Hoseini

Fallahpour

Wong

et al. 2021

Technological and Economic Development of Economy

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Assessing the performance of the Research and development (R&D) organizations to achieve higher productivity, growth, and development is always a critical necessity. Therefore, developing a more accurate model to evaluate the performance is always required. For this purpose, this study is aimed at developing a decision-making model for evaluating R&D performance. The model comes up with determining the most proper evaluative criteria for assessing R&D organizations. Then, it integrates Data Envelopment Analysis (DEA) with Analytical Network Process (ANP) to assess R&D performance. This paper is aimed to develop an integrated model for evaluating R&D performance. The findings of the study show that the DEA-ANP model is an accurate and acceptable model for evaluating R&D organizations’ performance.

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Section: Introductionmentioning

confidence: 99%