An Extended ORESTE Method with Interval-valued Hesitant Fuzzy Information for the Selection of Renewable Energy Projects

Abstract

Addressing global environmental problems and growing energy demand has highlighted the urgent need for a sustainable renewable energy program. The selection of suitable renewable energy sources is important for maintaining the balance between renewable energy supply and demand, reducing the cost of new energy applications, and accelerating the pace of the new energy revolution. In response to this issue, numerous multi-criteria decision-making (MCDM) methods exist, each aiming to improve group decision-making in complex scenarios involving multiple criteria. However, challenges remain, particularly in measuring and integrating hesitant evaluations and addressing issues related to weighting and normalization. This study introduces an extended French organization Rangement et Synthese de Ronnees Relationnelles (ORESTE) method combined with interval-valued hesitant fuzzy information. First, several novel distance measurement methods and a possibility degree formula for interval-valued hesitant fuzzy elements (IVHFEs) are developed. The proposed generalized normalized Hamming distance overcomes the limitations of existing normalized methods by handling IVHFEs of different lengths. Based on these developments, an extended ORESTE method incorporating interval-valued hesitant fuzzy information is established. To demonstrate its applicability, the proposed method is applied to a case study involving renewable energy project selection in China. The main contribution of this manuscript is proposing an extended fuzzy MCDM approach, and offering an alternative to the strong ranking procedure. The proposed method provides a technical path for relevant departments to make decisions on practical issues.

Keywords

Multi-criteria decision-making / ORESTE / interval-valued hesitant fuzzy information / renewable energy project

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