Measurement of overall performances of decision-making units using ideal and anti-ideal decision-making units

Azizi, Hossein; Ajirlu, Shahruz Fathi

doi:10.1016/j.cie.2010.05.013

Cited by 30 publications

(17 citation statements)

References 9 publications

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“…Various approaches for evaluating decision performance have been proposed [Azizi and Ajirlu 2010;Ray and Sahu 1990]. For example, decision performance is defined as the degree of confirmation to specifications of activities in relation to one or more of their desired values [Ray and Sahu 1990].…”

Section: Decision Performancementioning

confidence: 99%

Measurable Decision Making with GSR and Pupillary Analysis for Intelligent User Interface

Zhou

Sun

Chen

et al. 2015

ACM Trans. Comput.-Hum. Interact.

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This article presents a framework of adaptive, measurable decision making for Multiple Attribute Decision Making (MADM) by varying decision factors in their types, numbers, and values. Under this framework, decision making is measured using physiological sensors such as Galvanic Skin Response (GSR) and eyetracking while users are subjected to varying decision quality and difficulty levels. Following this quantifiable decision making, users are allowed to refine several decision factors in order to make decisions of high quality and with low difficulty levels. A case study of driving route selection is used to set up an experiment to test our hypotheses. In this study, GSR features exhibit the best performance in indexing decision quality. These results can be used to guide the design of intelligent user interfaces for decision-related applications in HCI that can adapt to user behavior and decision-making performance.

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Section: Decision Performancementioning

confidence: 99%

Measurable Decision Making with GSR and Pupillary Analysis for Intelligent User Interface

Zhou

Sun

Chen

et al. 2015

ACM Trans. Comput.-Hum. Interact.

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show abstract

“…The need to discriminate between efficient DMUs becomes more significant when the number of DMUs is small relative to the total number of inputs and outputs. Some studies (Andersen & Petersen, 1993;Azizi & Ajirlu, 2010;Bal, Orkcu, & Celebioglu, 2008;Doyle & Green, 1994;Lam, 2010;Li & Reeves, 1999;Sexton, Silkman, & Hogan, 1986;Tofallis, 1997) propose different approaches to enhance discrimination in DEA. Among those studies, Sexton et al (1986) and Doyle and Green (1994) suggest using the cross-efficiency matrix to measure efficiencies of DMUs.…”

Section: Respectivelymentioning

confidence: 98%

Minimizing deviations of input and output weights from their means in data envelopment analysis

Lam

Bai

2011

Computers & Industrial Engineering

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“…However if one stage is CRS and another is VRS, then the overall system still should be VRS. Thus it seems that the convexity constraints Instead we will only consider the case where both of the subsystems are VRS, as assumed in [17,45,46].…”

Section: (1) Production Possibility Sets Of Two-stage Systems In the mentioning

confidence: 99%

Two-stage DEA models with undesirable input-intermediate-outputs

Liu

Zhou

et al. 2015

Omega

135

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Measurement of overall performances of decision-making units using ideal and anti-ideal decision-making units

Cited by 30 publications

References 9 publications

Measurable Decision Making with GSR and Pupillary Analysis for Intelligent User Interface

Measurable Decision Making with GSR and Pupillary Analysis for Intelligent User Interface

Minimizing deviations of input and output weights from their means in data envelopment analysis

Two-stage DEA models with undesirable input-intermediate-outputs

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