scholarly journals Wind Farm Location Special Optimization Based on Grid GIS and Choquet Fuzzy Integral Method in Dalian City, China

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2454
Author(s):  
Liang Cui ◽  
Ye Xu ◽  
Ling Xu ◽  
Guohe Huang
Keyword(s):  
Integral Method ◽  
Wind Farm ◽  
Fuzzy Measure ◽  
Fuzzy Integral ◽  
Trade Offs ◽  
Gis Technique ◽  
Grid Gis ◽  
Dalian City ◽  
The Index System ◽  
Wind Farm Location

Selecting an appropriate wind farm location must be specific to a particular administrative region, which involves restrictions balance and trade-offs. Multi-criteria decision making (MCDM) and GIS are widely used in wind energy planning, but have failed to achieve the selection of an optimal location and make it difficult to establish a set of independent factors. Fuzzy measurement is an effective method to evaluate intermediate synthesis and calculates the factor weight through fuzzy integrals. In this paper, optimal wind farm location is analyzed through coupling Grid GIS technique with λ fuzzy measure. Dalian City is selected as the study area for proving the feasibility of the proposed method. Typography, meteorological, transmission facilities, biological passage, and infrastructure are taken into the index system. All the indexes are specialized into victor grid cells which are taken as the base wind farm location alternative unit. The results indicate that the Grid GIS based λ fuzzy measure and Choquet fuzzy integral method could effectively deal with the special optimization problem and reflect optimal wind farm locations.

scholarly journals Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2190 ◽  
Author(s):  
Rafael Dawid ◽  
David McMillan ◽  
Matthew Revie
Keyword(s):  
Wind Farm ◽  
Real Life ◽  
Synthetic Data ◽  
Optimal Solution ◽  
Decision Support Tool ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Support Tool ◽  
Trade Offs ◽  
The Impact

This paper for the first time captures the impact of uncertain maintenance action times on vessel routing for realistic offshore wind farm problems. A novel methodology is presented to incorporate uncertainties, e.g., on the expected maintenance duration, into the decision-making process. Users specify the extent to which these unknown elements impact the suggested vessel routing strategy. If uncertainties are present, the tool outputs multiple vessel routing policies with varying likelihoods of success. To demonstrate the tool’s capabilities, two case studies were presented. Firstly, simulations based on synthetic data illustrate that in a scenario with uncertainties, the cost-optimal solution is not necessarily the best choice for operators. Including uncertainties when calculating the vessel routing policy led to a 14% increase in the number of wind turbines maintained at the end of the day. Secondly, the tool was applied to a real-life scenario based on an offshore wind farm in collaboration with a United Kingdom (UK) operator. The results showed that the assignment of vessels to turbines generated by the tool matched the policy chosen by wind farm operators. By producing a range of policies for consideration, this tool provided operators with a structured and transparent method to assess trade-offs and justify decisions.

Building a Decision Dashboard for Improving Green Supply Chain Management

2018 ◽  
Vol 17 (05) ◽  
pp. 1363-1398 ◽  
Author(s):  
Yen-Ching Chuang ◽  
Shu-Kung Hu ◽  
James J. H. Liou ◽  
Huai-Wei Lo
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Integral Method ◽  
Green Supply Chain Management ◽  
Green Supply Chain ◽  
Fuzzy Integral ◽  
Chain Management ◽  
Network Process ◽  
Linear Aggregation ◽  
Green Suppliers

This paper proposes a decision model with a dashboard for systematically selecting and improving the performance of supplier in green supply chain management. The model combines both the decision-making trial and evaluation laboratory-based analytical network process (DANP) with a fuzzy integral method. The DANP method is used to construct the network system and derives the weights. The fuzzy integral method is applied to avoid inconsistent assumptions between the interdependence relation and linear aggregation for obtaining the aspiration-gaps of green suppliers. Last, the visualized dashboard allows for a clear representation of the interdependent-network structure of the attributes to assist companies in the selection of suitable suppliers, as well as assisting the suppliers in improving their performance with higher levels of service. A Taiwanese Electronics Company is demonstrated as an example. The results demonstrate that the dashboard can enable managers to make better decisions from a systematic perspective.

Combination of Pattern Classifiers Based on Naive Bayes and Fuzzy Integral Method for Biological Signal Applications

2020 ◽  
Vol 15 (2) ◽  
pp. 136-143
Author(s):  
Omid Akbarzadeh ◽  
Mohammad R. Khosravi ◽  
Mehdi Shadloo-Jahromi
Keyword(s):  
Integral Method ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Experimental Comparison ◽  
Classifier Combination ◽  
Fuzzy Integral ◽  
Combination Rules ◽  
Classifier Design ◽  
Combination Of Classifiers ◽  
Wide Range

Background: Achieving the best possible classification accuracy is the main purpose of each pattern recognition scheme. An interesting area of classifier design is to design for biomedical signal and image processing. Materials and Methods: In the current work, in order to increase recognition accuracy, a theoretical frame for combination of classifiers is developed. This method uses different pattern representations to show that a wide range of existing algorithms could be incorporated as the particular cases of compound classification where all the pattern representations are used jointly to make an accurate decision. Results: The results show that the combination rules developed under the Naive Bayes and Fuzzy integral method outperforms other classifier combination schemes. Conclusion: The performance of different combination schemes has been studied through an experimental comparison of different classifier combination plans. The dataset used in the article has been obtained from biological signals.

A (⋆, ∗)-Based Minkowski’s Inequality for Sugeno Fractional Integral of Order α > 0

2015 ◽  
Vol 18 (4) ◽  
Author(s):  
Azizollah Babakhani ◽  
Hamzeh Agahi ◽  
Radko Mesiar
Keyword(s):  
Fractional Integral ◽  
Fuzzy Measure ◽  
Minkowski’S Inequality ◽  
Fuzzy Integral ◽  
Binary Operations

AbstractWe first introduce the concept of Sugeno fractional integral based on the concept of g-seminorm. Then Minkowski’s inequality for Sugeno fractional integral of the order α > 0 based on two binary operations ⋆, ∗ is given. Our results significantly generalize the previous results in this field of fuzzy measure and fuzzy integral. Some examples are given to illustrate the results.

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