Abstract:PurposeThis study aims to improve the offshore wind farm (OWF) site selection evaluation index system and establishes a decision-making model for OWF site selection. It is expected to provide helpful references for the progress of offshore wind power.Design/methodology/approachFirstly, this paper establishes an evaluation criteria system for OWF site selection, considering six criteria (wind resource, environment, economic, technical, social and risk) and related subcriteria. Then, the Criteria Importance Thou… Show more
“…Yu et al [10] reported an integrated MCDM framework to choose a suitable OWFS based on the proximity indexed value approach using the interval 2-tuple linguistic set and synthetic weight model. In order to think over the psychological behavioral in the course of the decision, Zhao et al [11] developed a new study for selecting the worthwhile OWFS via the CRITIC method, cumulative prospect theory, and TOPSIS to an ideal solution methods within a spherical fuzzy setting. From the mentioned studies for the selection of OWFSs, we can find that no study merges the combination weight model and CoCoSo method to determine the optimal OWFS(s) within an indeterminacy setting.…”
The selection of offshore wind farm site (OWFS) has important strategic significance for vigorously developing offshore new energy and is deemed as a complicated uncertain multicriteria decision-making (MCDM) process. To further promote offshore wind power energy planning and provide decision support, this paper proposes a hybrid picture fuzzy (PF) combined compromise solution (CoCoSo) technique for prioritization of OWFSs. To begin with, a fresh PF similarity measure is proffered to estimate the importance of experts. Next, the novel operational rules for PF numbers based upon the generalized Dombi norms are defined, and four novel generalized Dombi operators are propounded. Afterward, the PF preference selection index (PSI) method and PF stepwise weights assessment ratio analysis (SWARA) model are propounded to identify the objective and subjective weight of criteria, separately. In addition, the enhanced CoCoSo method is proffered via the similarity measure and new operators for ranking OWFSs with PF information. Lastly, the applicability and feasibility of the propounded PF-PSI-SWARA-CoCoSo method are adopted to ascertain the optimal OWFS. The comparison and sensibility investigations are also carried out to validate the robustness and superiority of our methodology. Results manifest that the developed methodology can offer powerful decision support for departments and managers to evaluate and choose the satisfying OWFSs.
“…Yu et al [10] reported an integrated MCDM framework to choose a suitable OWFS based on the proximity indexed value approach using the interval 2-tuple linguistic set and synthetic weight model. In order to think over the psychological behavioral in the course of the decision, Zhao et al [11] developed a new study for selecting the worthwhile OWFS via the CRITIC method, cumulative prospect theory, and TOPSIS to an ideal solution methods within a spherical fuzzy setting. From the mentioned studies for the selection of OWFSs, we can find that no study merges the combination weight model and CoCoSo method to determine the optimal OWFS(s) within an indeterminacy setting.…”
The selection of offshore wind farm site (OWFS) has important strategic significance for vigorously developing offshore new energy and is deemed as a complicated uncertain multicriteria decision-making (MCDM) process. To further promote offshore wind power energy planning and provide decision support, this paper proposes a hybrid picture fuzzy (PF) combined compromise solution (CoCoSo) technique for prioritization of OWFSs. To begin with, a fresh PF similarity measure is proffered to estimate the importance of experts. Next, the novel operational rules for PF numbers based upon the generalized Dombi norms are defined, and four novel generalized Dombi operators are propounded. Afterward, the PF preference selection index (PSI) method and PF stepwise weights assessment ratio analysis (SWARA) model are propounded to identify the objective and subjective weight of criteria, separately. In addition, the enhanced CoCoSo method is proffered via the similarity measure and new operators for ranking OWFSs with PF information. Lastly, the applicability and feasibility of the propounded PF-PSI-SWARA-CoCoSo method are adopted to ascertain the optimal OWFS. The comparison and sensibility investigations are also carried out to validate the robustness and superiority of our methodology. Results manifest that the developed methodology can offer powerful decision support for departments and managers to evaluate and choose the satisfying OWFSs.
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