A Normal Wiggly Pythagorean Hesitant Fuzzy Bidirectional Projection Method and Its Application in EV Power Battery Recycling Mode Selection

Yang, Chengxiu; Wang, Qianzhe; Peng, Weidong; Zhang, Jiaqiang; Zhu, Jie

doi:10.1109/access.2020.2984242

Cited by 12 publications

(2 citation statements)

References 45 publications

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“…Chang et al, [11] applied hesitant fuzzy decision model to supplier selection of battery vehicles. Yang et al, [12] used the method of bidirectional projection using pythagorean hesitant fuzzy representations to select the recycling mode of power batteries of electric vehicles. Wilken et al, [13] compared electric vehicles with engine vehicles, Liu and Dai [14] used MOPSO and theory of cumulative prospect to make decisions on the charging stations of electric vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated Machine Learning Algorithms and MCDM Techniques in Optimal Ranking of Battery Electric Vehicles

Sudha,

Edwin,

Nivetha

2023

E3S Web of Conf.

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The automobile industries across the world of this present age are streamlining the manufacture of battery electric vehicles (BEV) as a step towards creating pollution free environment. BEVs are used as an alternate strategy to alleviate the carbon emission at global level. As environmental conservation is one of the long standing sustainable 1f ?developmental goals it is the need of the hour to make a paradigm shift from fossil fuels to renewable energy sources, at the same time this also gives rise to a decision-making problem on making optimal choice of the electric vehicles. In this paper a decision making problem based on ten alternative BEVs and eleven criteria is considered from the earlier works of Faith Ecer. The new ranking method of multi-criteria decision making MCRAT(Multiple Criteria Ranking by Alternative Trace) is used together with three different criterion weight computing methods of AHP(Analytical Hierarchy Process) ,CRITIC (CRiteria Importance Through Intercriteria Correlation) & MEREC (MEthod based on the Removal Effects of Criteria). The results obtained are compared and validated using random forest machine learning algorithm. This research work conjoins multi-criteria decision making methods and machine learning algorithms to make optimal decisions on Battery electric vehicles and this integrated approach yields optimal ranking results and it will certainly create new rooms in decision-making approaches in coming days.

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Section: Literature Reviewmentioning

confidence: 99%

Integrated Machine Learning Algorithms and MCDM Techniques in Optimal Ranking of Battery Electric Vehicles

Sudha,

Edwin,

Nivetha

2023

E3S Web of Conf.

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show abstract

“…Considering the advantages of NWHFS and TODIM, Liu and Zhang [31] defined the new distance measure of two NWHFEs and put forward an extended NWHF-TODIM method to handle the MADM problems under normal wiggly information, considering that if only membership functions represent a certain degree of the attributes, the importance of uncertainty becomes ignored. Based on the idea of Pythagorean hesitant fuzzy set, Yang et al [29] proposed a normal wiggly Pythagorean hesitant fuzzy set (NWPaHFS) that took into account both membership and nonmembership aspects. Besides, Narayanamoorthy et al [30] presented a normal wiggly dual hesitant fuzzy set (NWDHFS) as an extension of NWHFS and defined a new score function for the new fuzzy set.…”

Section: Introductionmentioning

confidence: 99%

Normal Wiggly Probabilistic Hesitant Fuzzy Information for Environmental Quality Evaluation

et al. 2021

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As a valuable tool for representing uncertain information, probabilistic hesitant fuzzy sets (PHFS) have gained considerable recognition and in-depth discussion in recent years to increase the flexibility and manifest hesitant information in decision-making problems. However, decision-makers (DMs) cannot express all preferences only through a few probabilistic terms in actual decision-making. Much critical information is hidden behind the original information provided by the DMs. Keeping that in mind, we are interested in mining deeper uncertain information from the original probabilistic hesitant fuzzy evaluation data. To achieve the target, we put forward a novel representation tool called the normal wiggly probabilistic hesitant fuzzy set (NWPHFS) to extract deeper uncertain preferences from original probabilistic information. NWPHFS retains the original evaluation information and carries and assesses the potential uncertain details for increasing the rationality of decision-making outcomes. Herein, we propose some fundamental concepts of NWPHFS, for instance, some elementary operational laws, distance measures between two NWPHFSs, and score function. We also suggest two new aggregation operators, that is, the normal wiggly probabilistic hesitant fuzzy weighted averaging (NWPHFWA) and normal wiggly probabilistic hesitant fuzzy weighted geometric (NWPHFWG). A novel mechanism is proposed here to work out multiattribute decision-making (MADM) in solving normal wiggly probabilistic decision-making problems. Through a practical example of environmental quality assessment, the specific calculation steps of this method are epitomized. Finally, we have demonstrated the feasibility and advancement of the proposed approach via a comprehensive comparative study.

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Cognitively Inspired Multi-attribute Decision-making Methods Under Uncertainty: a State-of-the-art Survey

Zhang

2022

Cogn Comput

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A Normal Wiggly Pythagorean Hesitant Fuzzy Bidirectional Projection Method and Its Application in EV Power Battery Recycling Mode Selection

Cited by 12 publications

References 45 publications

Integrated Machine Learning Algorithms and MCDM Techniques in Optimal Ranking of Battery Electric Vehicles

Integrated Machine Learning Algorithms and MCDM Techniques in Optimal Ranking of Battery Electric Vehicles

Normal Wiggly Probabilistic Hesitant Fuzzy Information for Environmental Quality Evaluation

Cognitively Inspired Multi-attribute Decision-making Methods Under Uncertainty: a State-of-the-art Survey

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