A robust group decision making framework using fuzzy TOPSIS and Monte Carlo simulation for wind energy projects multicriteria evaluation View Full Text


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Article Info

DATE

2022-07-07

AUTHORS

Athanasios P. Vavatsikos, Efstratios Tsesmetzis, Georgios Koulinas, Dimitrios Koulouriotis

ABSTRACT

Renewable-energy sector and especially wind energy exploitation gained a lot of attention during the last decades. However renewable sector investments' selection is constrained by a variety of spatially and non-spatially related factors. Moreover, citizens' susceptibility often resulted to call of promising investment plans, which were considered to be contradictory in terms of environmental and development aspects. Thus decision frameworks are needed to ensure transparency and consistency to the decision-making process. Such frameworks must consider multiple stakeholders’ opinions and preferences associated with both financial and sustainability issues capable of dealing with uncertainties derived from the inherent ambiguity related to the analysis data that estimated future conditions and Decision Makers' knowledge and preferences. The current research paper presents such a framework that combines a fuzzy extension of TOPSIS method and Monte Carlo Simulation to analyze the robustness of the selected solution avoiding average operator performance used in Group Decision-Making processes, which results to loss of information. The framework uses linguistic variables to estimate criterion weights and to rate alternatives provided by the decision makers. Then, Monte Carlo Simulation applied to the linguistic terms of the model, based on the distributions produced according to decision maker's preferences. The framework is implemented in a case study for the evaluation and selection of a wind-energy investment in Rhodope region range in Greece. More... »

PAGES

6055-6073

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12351-022-00725-x

DOI

http://dx.doi.org/10.1007/s12351-022-00725-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1149307349


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