Shannon’s entropy for combining the efficiency results of different DEA models: Method and application

Soleimani-damaneh, Majid; Zarepisheh, Masoud

doi:10.1016/j.eswa.2008.06.031

Cited by 100 publications

(66 citation statements)

References 13 publications

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“…[24]. Soleimani-Damaneh and Zarepisheh [24] first introduced Shannon's entropy into the DEA method and proposed an entropy DEA model.…”

Section: Dea With Shannon's Entropymentioning

confidence: 99%

“…[24]. Soleimani-Damaneh and Zarepisheh [24] first introduced Shannon's entropy into the DEA method and proposed an entropy DEA model. The main idea of this entropy DEA model is to aggregate the efficiency scores from different DEA models together based on the concept of Shannon's entropy.…”

Section: Dea With Shannon's Entropymentioning

confidence: 99%

“…Some literatures have combined the DEA method with Shannon's entropy. As far as we can say, the first such research was proposed by Soleimani-Damaneh and Zarepisheh [24] in which Shannon's entropy was used to aggregate efficiency scores from different DEA models. Based on this first research, Bian and Yang [25] proposed an extension to the resource and environment efficiency analysis of the provinces in China.…”

Section: Introductionmentioning

confidence: 99%

“…Based on this first research, Bian and Yang [25] proposed an extension to the resource and environment efficiency analysis of the provinces in China. More recently, Xie et al [26] proposed an extension research of [24] in which different variable subsets were considered. Similarly, Wu et al [27] proposed a method for aggregating cross-efficiency with Shannon's entropy and they also extended their research in [28].…”

Section: Introductionmentioning

confidence: 99%

“…Shannon's entropy [24] is a key concept in information theory. Some literatures have combined the DEA method with Shannon's entropy.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Determining Common Weights in Data Envelopment Analysis with Shannon’s Entropy

2014

Entropy

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Data Envelopment Analysis (DEA) is a non-parametric method for evaluating the efficiency of Decision Making Units (DMUs) with multiple inputs and outputs. In the traditional DEA models, the DMU is allowed to use its most favorable multiplier weights to maximize its efficiency. There is usually more than one efficient DMU which cannot be further discriminated. Evaluating DMUs with different multiplier weights would also be somewhat irrational in practice. The common weights DEA model is an effective method for solving these problems. In this paper, we propose a methodology combining the common weights DEA with Shannon's entropy. In our methodology, we propose a modified weight restricted DEA model for calculating non-zero optimal weights. Then these non-zero optimal weights would be aggregated to be the common weights using Shannon's entropy. Compared with the traditional models, our proposed method is more powerful in discriminating DMUs, especially when the inputs and outputs are numerous. Our proposed method also keeps in accordance with the basic DEA method considering the evaluation of the most efficient and inefficient DMUs. Numerical examples are provided to examine the validity and effectiveness of our proposed methodology.

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“…[24]. Soleimani-Damaneh and Zarepisheh [24] first introduced Shannon's entropy into the DEA method and proposed an entropy DEA model.…”

Section: Dea With Shannon's Entropymentioning

confidence: 99%

Section: Dea With Shannon's Entropymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Shannon's entropy [24] is a key concept in information theory. Some literatures have combined the DEA method with Shannon's entropy.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Determining Common Weights in Data Envelopment Analysis with Shannon’s Entropy

2014

Entropy

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Improving the performance of air conditioning systems by using phase change materials: A review

Omara

Abuelnour

2019

Int J Energy Res

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Summary This paper reviews the application of phase change materials (PCMs) for improving the performance of air conditioning systems. The different methods of integrating PCMs into air conditioning systems are presented. Moreover, the effects of PCM geometry, flow, and heat transfer characteristics on the performance of air conditioning systems and the potential use of PCMs in increasing the energy savings and coefficient of performance of air conditioning systems are also discussed. Recent studies on the thermodynamic (energy and exergy), economic, and environmental benefits of integrating PCMs into air conditioning systems are reviewed. Several methods for the preparation and optimal selection of PCMs are proposed to improve the performance of air conditioning systems, and then the challenges relating to PCM properties, optimal thickness, and PCM containers are highlighted. The economic aspects, humidity effect, life cycle assessment, and use of solid‐solid PCMs are cited as potentially important topics for future research.

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Controlling air pollution in a city: A perspective from SOAR‐PESTLE analysis

Gheibi

Karrabi

Mohammadi

et al. 2018

Integr Envir Assess & Manag

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Strengths, opportunities, aspirations, and results (SOAR) analysis is a strategic planning framework that helps organizations focus on their current strengths and opportunities to create a vision of future aspirations and the results they will bring. PESTLE is an analytical framework for understanding external influences on a business. This research paper describes a field study and interviews of city hall managers from the city of Mashhad, Iran, conducted to investigate the application of SOAR and PESTLE frameworks for managing Mashhad's air pollution. Strategies are prioritized by the technique for order of preference by similarity to ideal solution (TOPSIS), Shannon entropy (SE), and analytic network process (ANP) multicriteria decision-making (MCDM) methods, considering economic conditions, managers' opinions, consensus, city council approvals, and national documents. The results of this research study show that creating centralized databases, supporting local governments, and developing smart city infrastructure, with weights of 0.194, 0.182, and 0.161, respectively, are the highest ranked strategies for managing air pollution in Mashhad. It can also be concluded that citizen involvement is key to achieving success in the employment of any management strategy. Integr Environ Assess Manag 2018;14:480-488. © 2018 SETAC.

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Shannon’s entropy for combining the efficiency results of different DEA models: Method and application

Cited by 100 publications

References 13 publications

Determining Common Weights in Data Envelopment Analysis with Shannon’s Entropy

Determining Common Weights in Data Envelopment Analysis with Shannon’s Entropy

Improving the performance of air conditioning systems by using phase change materials: A review

Controlling air pollution in a city: A perspective from SOAR‐PESTLE analysis

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