Decision support framework for the risk ranking of agroforestry biomass power generation projects with picture fuzzy information

With the rapid growth of the global population and economy, energy consumption and demad are increasing sharply. As an essential renewable energy, biomass energy can promote the reform of energy production and consumption. Considering the characteristics of long investment cycle and large investment...

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Bibliographic Details
Published inJournal of intelligent & fuzzy systems Vol. 39; no. 3; pp. 4631 - 4650
Main Authors Zhou, Xiao-Yu, Wang, Xiao-Kang, Wang, Jian-qiang, Li, Jun-Bo, Li, Lin
Format Journal Article
LanguageEnglish
Published Amsterdam IOS Press BV 01.01.2020
Subjects
Agroforestry
Biomass energy
Biomass energy production
Consumption
Decision making
Decision support systems
Electric power generation
Energy consumption
Fuzzy logic
Fuzzy sets
Investment
Multiple criterion
Psychology
Ranking
Risk analysis
Risk assessment
Sensitivity analysis
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Summary:With the rapid growth of the global population and economy, energy consumption and demad are increasing sharply. As an essential renewable energy, biomass energy can promote the reform of energy production and consumption. Considering the characteristics of long investment cycle and large investment scale of agroforestry biomass power generation (AFBPG) projects, this study establishes a decision support framework for risk ranking of AFBPG project under picture fuzzy environment. The proposed framework considers not only the fuzziness and uncertainty of decision-making problems but also the decision-makers’ (DMs) psychological behavior. First, given the integrity of information representation, DMs provide risk assessment information expressed with picture fuzzy numbers, and then gives the distance of the picture fuzzy set (PFS) to maximize the PFS information. Second, the entropy weight method is used to compute the objective weight. Third, the VIKOR (Vlse Kriterijumska Optimizacija I Kompromisno Resenje) – TODIM (an acronym in Portuguese for an interactive multi-criteria decision making) method is suggested for ranking risk factors, which reflects the behavioral psychology of DMs. Moreover, the proposed evaluation model is successfully applied in a practical case. The results show that the model is valid for ranking risk factors under picture fuzzy environment. Last but not least, comparison and sensitivity analysis are implemented to verify the effectiveness and applicability of the proposed method and some suggestions for practical application are put forward.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-200575