An extended combined compromise solution framework based on novel intuitionistic fuzzy distance measure and score function with applications in sustainable biomass crop selection

Now-a-days, due to the increasing demand of the energy resources across the world, choosing the best sustainable biomass crop for the production of biofuels is a strategic decision-making problem. This is generally accepted that intuitionistic fuzzy sets (IFSs) are much more efficient in comparison with fuzzy sets at representing and processing the uncertainty in real-life problems. Proceeding on the same line, this paper attempts to introduce an extended combined compromise solution (CoCoSo) framework to analyze the sustainable biomass crop selection (SBCS) problem in an intuitionistic fuzzy environment. In this framework, we suggest a new integration function based on double normalization multiple aggregation approach to overcome the aggregation biases of the original CoCoSo approach and discuss its advantages with some numerical examples. We also develop a combined weighting strategy based on distance measure and decision experts’ (DEs) opinions to evaluate the significance of criteria . For this, we propose a novel distance measure (DM) and establish its superiority through some numerical comparisons. Also, the rationality of the suggested measure over the extant measures is justified by the use of an algorithm based on the developed measure for pattern recognition issues. In this framework, the comparison issue of IFSs is resolved by proposing a new score function. Furthermore, a case study of the SBCS is presented for the implementation of the developed CoCoSo approach, which confirms the viability and effectiveness of the new methodology. The results of the sensitivity analysis demonstrate that option “Miscanthus” consistently achieves the highest rank and is independent of variations of trade-off parameter and balancing factor. Finally, a comprehensive comparison is carried out to ensure the steadiness and reliability of the introduced framework.