TY - JOUR
T1 - A multi-granular linguistic distribution-based group decision making method for renewable energy technology selection
AU - Liang, Yingying
AU - Ju, Yanbing
AU - Martínez, Luis
AU - Dong, Peiwu
AU - Wang, Aihua
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/2
Y1 - 2022/2
N2 - The scarcity of resources requires a decrease in nonrenewable energy consumption, which progressively promotes the development of renewable energy due to its immense potential and environmental friendliness. Hence, the use of renewable energy technology is critical for realizing the economic effect, the environment effect and the social benefit unified. Generally, renewable energy technology selection is treated as a multiple criteria group decision making problem. However, decision makers are not allowed to express multiple preferences via personalized linguistic distribution assessments deliberating on diverse criteria in the existing approaches. This work proposes a multi-granular linguistic distribution-based group decision-making method by linking multi-granular linguistic distribution assessments and LINMAP (Linear Programming Technique for Multidimensional Analysis of Preference) method with a mathematical model that can simultaneously yield the credible weights of the considered criteria and prioritize the sequence of optimal renewable energy technologies. To this end, the linguistic distribution-based Hellinger distance measure and linguistic hierarchy-based multi-granular linguistic distribution transformation method are proposed. The decision framework is applied to a case study of power generation-based technology selection, generating reliable criteria weights and yielding acceptable outcomes based on collected assessments. Eventually, the sensitivity analysis and comparative analysis are conducted to verify the feasibility and practicability of our proposal. This flexible decision support technique is geared towards managers and strives to provide reference and inspiration for renewable energy technology selection.
AB - The scarcity of resources requires a decrease in nonrenewable energy consumption, which progressively promotes the development of renewable energy due to its immense potential and environmental friendliness. Hence, the use of renewable energy technology is critical for realizing the economic effect, the environment effect and the social benefit unified. Generally, renewable energy technology selection is treated as a multiple criteria group decision making problem. However, decision makers are not allowed to express multiple preferences via personalized linguistic distribution assessments deliberating on diverse criteria in the existing approaches. This work proposes a multi-granular linguistic distribution-based group decision-making method by linking multi-granular linguistic distribution assessments and LINMAP (Linear Programming Technique for Multidimensional Analysis of Preference) method with a mathematical model that can simultaneously yield the credible weights of the considered criteria and prioritize the sequence of optimal renewable energy technologies. To this end, the linguistic distribution-based Hellinger distance measure and linguistic hierarchy-based multi-granular linguistic distribution transformation method are proposed. The decision framework is applied to a case study of power generation-based technology selection, generating reliable criteria weights and yielding acceptable outcomes based on collected assessments. Eventually, the sensitivity analysis and comparative analysis are conducted to verify the feasibility and practicability of our proposal. This flexible decision support technique is geared towards managers and strives to provide reference and inspiration for renewable energy technology selection.
KW - LINMAP method
KW - Linguistic distribution assessments
KW - Multi-granular linguistic information
KW - Multiple criteria group decision making
KW - Renewable energy technology selection
UR - http://www.scopus.com/inward/record.url?scp=85123030208&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2021.108379
DO - 10.1016/j.asoc.2021.108379
M3 - Article
AN - SCOPUS:85123030208
SN - 1568-4946
VL - 116
JO - Applied Soft Computing
JF - Applied Soft Computing
M1 - 108379
ER -