A novel hybrid multi-criteria decision-making approach for offshore wind turbine selection

Xu, Li; Xu, Li; Cai, Jingjing; Cao, Jing; Zhao, Fengfeng; Zhang, Jiaying

doi:10.1177/0309524x20973600

Cited by 12 publications

(5 citation statements)

References 24 publications

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“…By reconstructing the analytical network process (ANP) and entropy weight method (EWM), a new hybrid multi-criteria decision (MCDM) method for offshore wind turbine selection is designed. Based on the weights assigned to ANP and EWM, the best alternative can be selected [ 51 ].…”

Section: Literature Review and Methodologymentioning

confidence: 99%

A Hybrid Multi-Criteria Decision-Making Approach Based on ANP-Entropy TOPSIS for Building Materials Supplier Selection

Chen¹

2021

Entropy

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This article will tell you how to combine “entropy” in the model to reduce the bias of multi-criteria evaluation. Subjective weights are usually determined by decision makers based on their professional background, experience and knowledge, and other factors. The objective weight is obtained by constructing an evaluation matrix of the information based on the actual information of the evaluation criteria of the scheme, and obtained through multi-step calculations. Different decision-making methods are based on different weight types. Considering only one of the two weights often leads to biased results. In addition, in order to establish an effective supply chain, buyers must find suitable merchants among suppliers that provide quality products and/or services. Based on the above factors, it is difficult to choose a suitable alternative. The main contribution of this paper is to combine analytic network process (ANP), entropy weight and the technique for order preference by similarity to an ideal solution (TOPSIS) to construct a suitable multi-criteria decision (MCDM) model. By means of ANP-entropy weights to extend the TOPSIS method, ANP-entropy weights are used to replace subjective weights. A supplier selection decision-making model based on ANP-entropy TOPSIS is proposed. At last, the sensitivity analysis shows that, taking the selection of building materials suppliers as an example, the hybrid ANP-entropy TOPSIS method can effectively select suitable suppliers.

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Section: Literature Review and Methodologymentioning

confidence: 99%

A Hybrid Multi-Criteria Decision-Making Approach Based on ANP-Entropy TOPSIS for Building Materials Supplier Selection

Chen¹

2021

Entropy

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“…Reference [79] proposes a new method based on TOPSIS, which is universal and verified by using real Saudi Arabian datasets. Innovative approaches in this field have incorporated fuzzy logic to allow for flexible decision-making rules in turbine selection [80], the application of SWARA methods to enhance decision quality in turbine selection [81], and the use of hybrid MCDM techniques combining the analytic network process (ANP) and entropy weight method (EWM) for turbine choice in specific offshore wind projects [82]. The integration of fuzzy preference programming has been explored to build sophisticated decision models for turbine selection [83,84].…”

Section: Turbine Selection For Offshore Wind Farmsmentioning

confidence: 99%

“…The SWARA method is utilized for weighting process, and it has not been used in previous relevant research. [82] Technology, adaptability to wind resources, economy impact, historical achievements, supplier services ANP-EWM Subjective ANP is combined with objective EWM to fully leverage their respective strengths in selecting wind turbines. [83] Reliability, economic impact, supplier services FPP-ANP Triangular fuzzy numbers and fuzzy comparison matrices are introduced, and fuzzy preference programming (FPP) is combined with the analytic network process to construct a fuzzy analytic network process (FANP) unit selection model.…”

Section: Swara-svns-topsismentioning

confidence: 99%

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

Song,

Shen,

Huang

et al. 2024

JMSE

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As global energy crises and climate change intensify, offshore wind energy, as a renewable energy source, is given more attention globally. The wind power generation system is fundamental in harnessing offshore wind energy, where the control and design significantly influence the power production performance and the production cost. As the scale of the wind power generation system expands, traditional methods are time-consuming and struggle to keep pace with the rapid development in wind power generation systems. In recent years, artificial intelligence technology has significantly increased in the research field of control and design of offshore wind power systems. In this paper, 135 highly relevant publications from mainstream databases are reviewed and systematically analyzed. On this basis, control problems for offshore wind power systems focus on wind turbine control and wind farm wake control, and design problems focus on wind turbine selection, layout optimization, and collection system design. For each field, the application of artificial intelligence technologies such as fuzzy logic, heuristic algorithms, deep learning, and reinforcement learning is comprehensively analyzed from the perspective of performing optimization. Finally, this report summarizes the status of current development in artificial intelligence technology concerning the control and design research of offshore wind power systems, and proposes potential future research trends and opportunities.

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“…As a result, Abdel-Basset et al established a site selection evaluation system for offshore wind turbines by comprehensively considering six indicators: wind resources, construction, economy, environment, society, and risk, along with related secondary indicators [21] . Yu et al put forward a standard system for evaluating offshore wind turbines, including technical reliability, resources, economy, environmental impact and supplier performance [22] . Offshore wind turbines have a very complex operating environment, which makes maintenance time-consuming and expensive.…”

Section: Introductionmentioning

confidence: 99%

A life cycle decision framework of China offshore wind turbines with ANP-Intuitionistic fuzzy TOPSIS method

Zhou,

Wang,

et al. 2024

Green Manuf Open

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In China, offshore wind energy is a popular source of green energy. Selecting offshore wind turbine design schemes is a decision problem based on multiple criteria. However, the selection is hardly made from the point of view of the life cycle due to the complex association of evaluation criteria, especially in the conceptual design stage. To solve this problem, a new multi-criteria decision-making framework for selecting the life cycle design scheme of offshore wind turbines is designed. The design information of the life cycle process of these turbines is expressed using Function-Structure-Material-Process-Transportation model in this framework. The life cycle evaluation index system of offshore wind turbines was established and mapped with this model to carry out rapid evaluation of various schemes. In light of this, an intuitive fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method based on analytic network process (ANP) was proposed to process fuzzy decision information and establish criteria correlation. Finally, a case study was conducted, and sensitivity and comparative analyses were carried out. The proposed method can effectively select the optimal scheme from six different design schemes for the life cycle of offshore wind turbines.

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A novel hybrid multi-criteria decision-making approach for offshore wind turbine selection

Cited by 12 publications

References 24 publications

A Hybrid Multi-Criteria Decision-Making Approach Based on ANP-Entropy TOPSIS for Building Materials Supplier Selection

A Hybrid Multi-Criteria Decision-Making Approach Based on ANP-Entropy TOPSIS for Building Materials Supplier Selection

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

A life cycle decision framework of China offshore wind turbines with ANP-Intuitionistic fuzzy TOPSIS method

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