Single-Valued Neutrosophic Distance Measure-Based Merec-Rancom-Wisp for Solving Sustainable Energy Storage Technology Problem

Artificial Intelligence Computer Science Engineering Information Science Energy Engineering
Single-Valued Neutrosophic Set Energy Storage Technology Distance Measure Merec Method Rancom Method Wisp Method Multi-Criteria Decision Making

Academic Background

With the continuous growth of global energy demand, Energy Storage Technology (EST) plays a crucial role in mitigating environmental impacts and reducing carbon footprints. EST is not only an essential component of renewable energy but also a key factor in decarbonizing the global energy structure. However, selecting the appropriate EST involves multiple sustainability considerations, making the decision-making process complex and fraught with uncertainty. Traditional decision-making methods often fall short when dealing with such multi-criteria, uncertain, and inconsistent problems.

To address this issue, the authors propose a hybrid Multi-Criteria Group Decision-Making (MCGDM) method based on the Single-Valued Neutrosophic Set (SVNS). As an extension of fuzzy sets, SVNS can better handle uncertain, inconsistent, and ambiguous data in real-world decision-making. By introducing SVNS, the authors aim to develop a new decision-making framework to evaluate and prioritize different ESTs, providing more reasonable solutions in complex decision-making environments.

Source of the Paper

This paper is co-authored by Arunodaya Raj Mishra, Dragan Pamucar, Pratibha Rani, and Ibrahim M. Hezam. The authors are affiliated with various research institutions, including the Vellore Institute of Technology in India, the University of Belgrade in Serbia, the Indian Institute of Technology (BHU) Varanasi in India, and King Saud University in Saudi Arabia. The paper was accepted on March 5, 2025, and published in the journal Cognitive Computation with the DOI 10.1007/s12559-025-10437-x.

Research Process and Results

Research Process

  1. Determination of Decision Experts’ Weights
    The study first introduces a novel Single-Valued Neutrosophic Hellinger Distance Measure to calculate the weights of Decision Experts (DEs). This distance measure effectively distinguishes differences between SVNSs, providing a scientific basis for assigning weights to decision experts. Specifically, the authors use SVNS to represent the opinions of decision experts and calculate the differences between experts using the Hellinger distance measure. Finally, the weights of each expert are determined by combining the SVNS score function.

  2. Construction of the Aggregated Single-Valued Neutrosophic Decision Matrix (ASVN-DM)
    After determining the weights of decision experts, the authors use the Single-Valued Neutrosophic Weighted Average (SVNWA) operator to aggregate the opinions of different experts, constructing an aggregated single-valued neutrosophic decision matrix. This step ensures that all expert opinions are reasonably integrated, providing foundational data for subsequent decision analysis.

  3. Determination of Criteria Weights
    The authors propose a method for determining criteria weights by combining objective and subjective weights. The objective weights are calculated using the “Method based on the Removal Effects of Criteria” (MEREC), while the subjective weights are determined using the “Ranking Comparison” (RANCOM) tool. Finally, the authors integrate these two types of weights to obtain the integrated weight for each criterion.

  4. Construction of the Normalized ASVN-DM
    To further the analysis, the authors perform linear and vector normalization on the ASVN-DM, ultimately constructing an averaged normalized ASVN-DM. This step ensures that data across different criteria are comparable, providing a basis for subsequent weighted sum and weighted product analysis.

  5. Weighted Sum and Weighted Product Analysis
    The authors use the weighted sum and weighted product methods to evaluate each EST. Specifically, they calculate the Weighted Sum Deviation (WSD) and Weighted Sum Ratio (WSR) and use these metrics to provide an initial ranking of ESTs. Then, the authors calculate the Weighted Product Deviation (WPD) and Weighted Product Ratio (WPR) to further validate the ranking’s rationality.

  6. Calculation of the Improved Utility Degree (IUD)
    To map the results of the weighted sum and weighted product analysis to the [0,1] range, the authors calculate the Improved Utility Degree (IUD) for each EST. This step ensures that all evaluation metrics have a unified scale, providing a scientific basis for the final decision.

  7. Calculation of the Overall Utility Degree (OUD) and Ranking
    Finally, the authors calculate the Overall Utility Degree (OUD) for each EST by integrating the results of the weighted sum and weighted product analysis. Based on the OUD values, the ESTs are ranked. The results show that lithium-ion batteries (B1) perform the best among all ESTs, followed by lead-acid batteries (B2), sodium-sulfur batteries (B3), and flow batteries (B4).

Research Results

  1. Determination of Decision Experts’ Weights
    By introducing the new Hellinger distance measure, the authors successfully calculated the weights of each decision expert and verified the effectiveness of this distance measure in handling SVNS data.

  2. Determination of Criteria Weights
    By combining the MEREC and RANCOM methods, the authors successfully calculated the integrated weights for each criterion and verified the rationality of this method in handling multi-criteria decision-making problems.

  3. Evaluation and Ranking of ESTs
    Through weighted sum and weighted product analysis, the authors evaluated each EST and ultimately determined that lithium-ion batteries (B1) are the optimal choice. This result is consistent with existing literature, validating the effectiveness and practicality of the proposed method.

Conclusions and Significance

Conclusions

This study proposes a hybrid multi-criteria group decision-making method based on the Single-Valued Neutrosophic Set, successfully addressing the complex decision-making problems in sustainable energy storage technology selection. By introducing the new Hellinger distance measure, combining the MEREC and RANCOM methods for criteria weight determination, and using weighted sum and weighted product analysis, the authors provide a scientific and rational decision-making framework for EST selection.

Scientific and Application Value

  1. Scientific Value
    This study is the first to combine the Single-Valued Neutrosophic Set with the Hellinger distance measure, proposing a new method for determining decision experts’ weights. Additionally, the authors are the first to combine the MEREC and RANCOM methods, proposing a new method for determining criteria weights. These innovations provide new research ideas and methods for the field of multi-criteria decision-making.

  2. Application Value
    The method proposed in this study is not only applicable to energy storage technology selection but can also be extended to other complex multi-criteria decision-making problems. For example, it can be applied to supply chain management, investment decisions, environmental assessments, and other fields, providing a scientific basis for practical decision-making.

Research Highlights

  1. New Hellinger Distance Measure
    This study is the first to apply the Hellinger distance measure to the Single-Valued Neutrosophic Set, effectively addressing the issue of measuring differences between SVNS data.

  2. Combination of MEREC and RANCOM for Criteria Weight Determination
    This study is the first to combine the MEREC and RANCOM methods, proposing a new method for determining criteria weights, effectively addressing the issue of weight allocation in multi-criteria decision-making.

  3. Weighted Sum and Weighted Product Analysis
    Through weighted sum and weighted product analysis, this study provides a scientific and rational decision-making framework for EST selection, validating the effectiveness and practicality of the proposed method.

Other Valuable Information

This study also verifies the superiority of the proposed method in handling multi-criteria decision-making problems through comparative analysis. Compared to other existing multi-criteria decision-making methods, the proposed method performs better in handling uncertain, inconsistent, and ambiguous data, providing more scientific and rational solutions for practical decision-making.

Through this study, the authors not only provide a scientific and rational decision-making framework for sustainable energy storage technology selection but also offer new research ideas and methods for the field of multi-criteria decision-making. This research holds significant scientific and application value, providing strong support for practical decision-making.