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Cloud decision model for selecting sustainable energy crop based on linguistic intuitionistic information

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  • Hong-Gang Peng
  • Jian-Qiang Wang

Abstract

In recent years, sustainable energy crop has become an important energy development strategy topic in many countries. Selecting the most sustainable energy crop is a significant problem that must be addressed during any biofuel production process. The focus of this study is the development of an innovative multi-criteria decision-making (MCDM) method to handle sustainable energy crop selection problems. Given that various uncertain data are encountered in the evaluation of sustainable energy crops, linguistic intuitionistic fuzzy numbers (LIFNs) are introduced to present the information necessary to the evaluation process. Processing qualitative concepts requires the effective support of reliable tools; then, a cloud model can be used to deal with linguistic intuitionistic information. First, LIFNs are converted and a novel concept of linguistic intuitionistic cloud (LIC) is proposed. The operations, score function and similarity measurement of the LICs are defined. Subsequently, the linguistic intuitionistic cloud density-prioritised weighted Heronian mean operator is developed, which served as the basis for the construction of an applicable MCDM model for sustainable energy crop selection. Finally, an illustrative example is provided to demonstrate the proposed method, and its feasibility and validity are further verified by comparing it with other existing methods.

Suggested Citation

  • Hong-Gang Peng & Jian-Qiang Wang, 2017. "Cloud decision model for selecting sustainable energy crop based on linguistic intuitionistic information," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(15), pages 3316-3333, November.
  • Handle: RePEc:taf:tsysxx:v:48:y:2017:i:15:p:3316-3333
    DOI: 10.1080/00207721.2017.1367433
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    References listed on IDEAS

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    1. Alizadeh, Reza & Soltanisehat, Leili & Lund, Peter D. & Zamanisabzi, Hamed, 2020. "Improving renewable energy policy planning and decision-making through a hybrid MCDM method," Energy Policy, Elsevier, vol. 137(C).

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