A Bipolar Complex Fuzzy CRITIC-ELECTRE III Approach Using Einstein Averaging Aggregation Operators for Enhancing Decision Making in Renewable Energy Investments

Abstract

Faced with rapidly rising energy demand in industrialised societies and widespread global concern, countries are actively promoting the transition from conventional to renewable energy systems. The goal is to invest in renewable energy in the most efficient way to meet rising energy demand and reduce the challenges posed by climate change. However, decision makers must carefully weigh various factors when selecting the most appropriate renewable energy investment projects. This paper presents a novel method for Multi-Attribute Decision Making(MADM) that uses the Bipolar Complex Fuzzy(BCF) to convey the vagueness and uncertainty of decision makers, so that the result obtained better reflects the actual scenario and the subjective biases of decision makers. We defined BCF Einstein Weighted Averaging (BCFEWA) operator and BCF Einstein Ordered Weighted Averaging (BCFEOWA) operator to aggregate evaluation information. Then we discussed some properties of the proposed aggregation operators. Additionally, we present an integrated MADM technique grounded in the BCF framework that combines the CRiteria Importance Through Intercriteria Correlation (CRITIC) and ELECTRE III methods. Specifically, the CRITIC method determines attribute weights, and the ELECTRE III method ranking the alternatives to determine the best renewable energy investment projects. After analysing the results and comparisons, it can be inferred that the suggested methodology offers an effective evaluation process.