- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Renewable energy is the inevitable choice for the sustainable development of society and economy. How to select the most appropriate renewable energy for a region is a complex multicriterion decision making (MCDM) problem. Taking Jilin Province as an example, this paper proposes a new MCDM method. In order to better express the hesitancy, inconsistency and uncertainty of decision makers’ preferences, linguistic hesitant fuzzy set (LHFS) is proposed. On the basis of cloud model, the rule of transforming LHFS to quantitative values is defined. Subsequently, the distance measure and support measure are established. In consideration of the interdependency of criteria, an LHFS aggregation operator based on improved Choquet integral is proposed. Finally, the ranking result of the aggregated LHFS corresponding to each renewable energy alternative is obtained according to the expectation values. The result shows that the preferred renewable energy for Jilin is biomass energy, followed by wind energy, hydro energy and solar energy. The validation analysis and comparison analysis are given to demonstrate the effectiveness of the proposed method.
According to Jilin’s five-year plan, renewable energy development is an important issue for government. The selection of renewable energy can be regarded as an MCDM problem. This paper presents an MCDM method with linguistic hesitant fuzzy sets, which combines hesitant fuzzy sets with linguistic fuzzy sets to express the complexity of uncertain environment and the vagueness of human cognition. The proposed approach uses cloud model to handle the randomness and fuzziness from the subjective judgments of experts and defines an improved Choquet integral operator to aggregate LHFSs in consideration of the interdependency of criteria. In terms of Jilin, the most suitable renewable energy is biomass energy, followed by wind energy, hydro energy and solar energy. Subsequently, we adopt three criteria to test the method and compare the method with other methods, verifying the validity and superiority of the proposed method. The main contributions of this paper are: (1) It introduces LHFS to express the evaluation information of renewable energy MCDM problem, which is the first application of LHFS in renewable energy selection. (2) It defines an improved Choquet integral operator to aggregate LHFSs. Based on the defined operator and cloud model, a new MCDM method with universality is proposed, which can be applied to not only renewable energy selection problem but also other MCDM problems. (3) It uses the concept of comprehensive cloud instead of the deviation of decision results by extending the shorter LHFS.