Fuzzy random regression based multi-attribute evaluation and its application to oil palm fruit grading

Arbaiy, Nureize; Watada, Junzo; Wang, S.

doi:10.1007/s10479-011-0979-z

Cited by 15 publications

(12 citation statements)

References 30 publications

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“…Meanwhile, fuzzy random variables with confidence intervals are applied in the fuzzy multi attribute decision making to enable decision makers to evaluate and find the importance weight ( [10]; [11]). Determining weight in multi-attribute decision making is important, so we propose a fuzzy random regression to estimate attribute importance in total evaluation within the bound of hybrid uncertainty ( [11]; [12]). Moreover, the fuzzy random regression method also has been proposed as an integral component of regression models in handling the existence of fuzzy random information ( [12]; [20]; [21]).…”

Section: Background Studymentioning

confidence: 99%

“…Determining weight in multi-attribute decision making is important, so we propose a fuzzy random regression to estimate attribute importance in total evaluation within the bound of hybrid uncertainty ( [11]; [12]). Moreover, the fuzzy random regression method also has been proposed as an integral component of regression models in handling the existence of fuzzy random information ( [12]; [20]; [21]). The fuzzy regression model is written as the following linear programming problem:…”

Section: Background Studymentioning

confidence: 99%

“…Granule weight estimation. This step is taken to estimate the weight of the granules considered in the evaluation which is based on fuzzy random regression analysis ( [11]; [12] …”

Section: Multi-granular Evaluation Scheme Modelmentioning

confidence: 99%

“…A relevant measurement scale is used to assign value for this granule [11]. On the other hand, the rating of each sample data is performed with respect to each granule and the weights given to each granule [12].…”

Section: Introductionmentioning

confidence: 99%

“…The weighting determines the relationship, that is multi-granular is characterized. The evaluation models were primarily developed for a crisp value environment and have been widely applied in various real-world applications ( [8]; [12]; [11]; [13]). As decision-making includes uncertain and vague information, fuzzy set theory was introduced to such decision making structure.…”

Section: Introductionmentioning

confidence: 99%

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Multi-granules evaluation model through fuzzy random regression analysis

Arbaiy

2014

2014 IEEE International Conference on Industrial Engineering and Engineering Management

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Various approaches and models are introduced to facilitate better information extraction and decision making. Granular computation is one of the approaches which provide ability to extract information granules which encompasses a collection of entities. A relationship is used to connect granule and levels and further forms information granule model. This study emphasizes information granulation in the fuzzy random based environment. Dealing with data containing fuzzy and random characteristic is complicated in some way though this kind of data are exist in real world application and requires proper handling. Thus, a fuzzy random based regression is introduced to improve the extraction of weight of granules in building a multi-granular decision making scheme. The proposed model is able to organize decision or preference provided by evaluators in order to compute collective assessments about the product samples that will be used by the decision maker to determine final decision. A numerical example shows the usability of the model and presents the advantage of the proposed method.

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Section: Background Studymentioning

confidence: 99%

Section: Background Studymentioning

confidence: 99%

“…Granule weight estimation. This step is taken to estimate the weight of the granules considered in the evaluation which is based on fuzzy random regression analysis ( [11]; [12] …”

Section: Multi-granular Evaluation Scheme Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%