Synthetic Evaluation of Oilfield Development Plans Based on a Cloud Model

Liu, Zhibin; Liu-li, LU; Zhang, Zhiyong; Chen, Yi-Shen

doi:10.1080/23317000.2014.915771

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Although 2-tuple linguistic representation model can avoid information loss, it cannot reflect the membership and non-membership degrees of an element to a certain concept. Inspired by intuitionistic fuzzy sets, Liu et al (2014b) introduced intuitionistic linguistic sets (ILSs). After Wang and Li, some experts give a few extensions of ILSs, such as intuitionistic uncertain linguistic sets (IULSs) (Liu and Jin, 2012) and interval-valued intuitionistic uncertain linguistic sets (IVIULSs) (Xu and Shen, 2014).…”

Section: Introductionmentioning

confidence: 99%

2-Tuple Linguistic Hesitant Fuzzy Aggregation Operators and Its Application to Multi-Attribute Decision Making

Tan¹,

Jia²,

Chen³

2017

Informatica

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In this paper, a new class of uncertain linguistic variables called 2-tuple linguistic hesitant fuzzy sets (2-TLHFSs) is defined, which can express complex multi-attribute decision-making problems as well as reflect decision makers' hesitancy, uncertainty and inconsistency. Besides, it can avoid information and precision losing in aggregation process. Firstly, several new closed operational laws based on Einstein t-norm and t-conorm are defined over 2-TLHFSs, which can overcome granularity and logical problems of existing operational laws. Based on the new operational laws, 2-tuple linguistic hesitant fuzzy Einstein weighted averaging (2-TLHFEWA) operator and 2-tuple linguistic hesitant fuzzy Einstein weighted geometric (2-TLHFEWG) operator are proposed, and some of their properties are investigated. Then, a new model method based on similarity to ideal solution is proposed to determine weights of attribute, which takes both subjective and objective factors into consideration. Finally, a linguistic hesitant fuzzy multi-attribute decision making procedure is developed by means of 2-TLHFEWA and 2-TLHFEWG operators. An example is given to illustrate the practicality and efficiency of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

2-Tuple Linguistic Hesitant Fuzzy Aggregation Operators and Its Application to Multi-Attribute Decision Making

Tan¹,

Jia²,

Chen³

2017

Informatica

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show abstract

“…The cloud model integrates the randomness and the fuzziness of the concepts and can be used in Data-Mining and Knowledge Discovery (DMKD), Spatial Data-Mining, and Knowledge Discovery (SDMKD) [27]. In 2014, Lund et al presented an evaluation model based on the cloud theory for the general engineering development programming [28,29], which can help sort the development plans reasonably in a certain degree. Cloud model combines randomness and fuzziness to reveal the correlation between them, which reflects the uncertainty of concept.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Evaluation of Offshore Oilfield Development Plans Based on Grey Clustering Analysis with Cloud Model

Min

Wen

et al. 2018

Mathematical Problems in Engineering

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Taking the development plans of an offshore oilfield as an example, a new comprehensive evaluation method, the improved Grey Clustering Analysis based on the cloud model (GCAC), is presented in this paper, which takes the ambiguity, randomness, and uncertainty of data into account and overcomes the limits of the general methods, such as subjective prejudice and objective randomness. GCAC converts the data of the oilfield development plans into a cloud, which considers the data of fuzziness, randomness, and the relationship between them. The grey membership degree of each development plan is calculated by this cloud model and an improved grey whitened function is presented in this paper. Then the plans are reordered by their grey membership degrees. In order to make more reasonable consideration of the artificial or unartificial uncertainties, GCAC combines the Grey Entropy Weighting method, Analytical Hierarchy Process (AHP), and Expert Assessment method to determine the weights of each level of indexes, which makes the weights more reasonable and reduces the randomness and the fuzziness of data. GCAC can help obtain a better comparison between the development plans. The reliability of this method is verified by the calculation results.

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Influences of EVs on Power System by Improving the Microclimate

Cao

Kuang

et al. 2016

Power Systems

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Synthetic Evaluation of Oilfield Development Plans Based on a Cloud Model

Cited by 4 publications

References 5 publications

2-Tuple Linguistic Hesitant Fuzzy Aggregation Operators and Its Application to Multi-Attribute Decision Making

2-Tuple Linguistic Hesitant Fuzzy Aggregation Operators and Its Application to Multi-Attribute Decision Making

Comprehensive Evaluation of Offshore Oilfield Development Plans Based on Grey Clustering Analysis with Cloud Model

Influences of EVs on Power System by Improving the Microclimate

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