*Important notice: This news reports on an unedited version of an accepted paper and is awaiting final editing. Therefore, the paper should not be regarded as conclusive or treated as established information.
A hybrid decision-making framework aimed at helping mining companies evaluate operational strategies under certain conditions has been developed. The framework combined the CRITIC (criteria importance through intercriteria correlation) method with the WASPAS (weighted aggregated sum product assessment) approach within a CPyFS (circular Pythagorean fuzzy set) system.
Study: Risk management and operational efficiency in underground coal mining using circular Pythagorean fuzzy CRITIC WASPAS approach with prioritized weights. Image Credit: Parilov/Shutterstock.com
The findings, published in Scientific Reports, demonstrate the method’s potential to support safer and more efficient data-driven decision-making in complex environments.
Addressing Challenges in Underground Coal Mining
Underground coal mining remains one of the most challenging industrial activities, as operators must continuously balance worker safety, equipment reliability, and production efficiency. Decisions often involve multiple technical, operational, and organizational factors that interact with one another.
At the same time, mining environments are highly uncertain. Geological conditions, equipment performance, human factors, and operational risks can change rapidly, making reliable decision-making difficult.
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Researchers have developed several multi-criteria decision-making (MCDM) methods to evaluate mining strategies. Many of these approaches rely on traditional fuzzy set theory to represent uncertainty. However, conventional fuzzy models have limited ability to capture complex uncertainty because they only consider membership and non-membership values.
In this study, the researchers developed a hybrid CRITIC–WASPAS framework within the CPyFS environment. The CRITIC method objectively determines the importance of each evaluation criterion using the available data, while the WASPAS method ranks alternative strategies by combining additive and multiplicative scoring approaches. Together, these methods create a reliable decision-support framework that could aid safer and more efficient mine planning.
Developing an Objective Decision-Support Framework
The researchers developed the decision-making framework using a multi-criteria group decision-making approach. The model combines CPyFS sets with the CRITIC weighting technique and the WASPAS ranking method. This combination allows the framework to evaluate multiple alternatives while accounting for uncertainty in expert judgments.
The study assessed five operational strategies using five evaluation criteria. These included technological integration, maintenance and asset reliability, safety culture and leadership commitment, operational efficiency, and overall safety performance.
Three decision-makers independently evaluated each strategy using predefined linguistic terms ranging from negligible to superior. The framework converted these qualitative assessments into circular Pythagorean fuzzy values before combining them into a single decision matrix.
The CRITIC method then calculated objective criterion weights based on data variability and correlations between criteria rather than subjective opinions. After determining the criterion weights, the WASPAS method normalized the decision matrix and ranked the alternatives by combining weighted sum and weighted product models. This hybrid approach produced stable rankings while reducing the influence of individual evaluator bias.
The researchers demonstrated the framework using a hypothetical underground coal mining case based on realistic industrial conditions. They also performed sensitivity analysis by varying model parameters and compared the results with established decision-making methods. These additional analyses helped evaluate the stability, consistency, and reliability of the proposed framework before considering future real-world applications.
Evaluating Mining Strategies Under Uncertainty
The analysis showed that alternative A2, which focuses on smart mining technologies for risk reduction and efficiency improvement, achieved the highest overall ranking. This strategy combines digital technologies such as automation, real-time monitoring, artificial intelligence, and advanced data analytics to improve both safety and operational performance.
Alternative A5, which emphasizes risk control and productivity optimization, ranked second, followed by A3, A4, and A1. With further development, the strategies could help to enable earlier hazard detection, reduce human error, and improve equipment utilization.
It is thought that real-time monitoring and automated decision support could allow operators to respond more quickly to changing underground conditions, reducing both operational risks and production delays. These findings highlight the growing importance of digital technologies in modern underground mining operations.
The study also demonstrated the advantages of combining the CRITIC and WASPAS methods within the CPyFS framework. Unlike traditional fuzzy approaches, CPyFS includes a radius parameter in addition to membership and non-membership values. The authors believe the radius parameter gives the framework greater flexibility when multiple criteria and conflicting objectives must be considered simultaneously.
Many existing decision-making methods assign criterion weights based largely on expert opinion, which can introduce bias into the final rankings. The CRITIC method instead calculates weights based on the variability of each criterion and its correlations with other criteria.
This data-driven approach reduces subjectivity while ensuring that more influential criteria receive greater importance during the evaluation process. Within the hypothetical case study, the model produced stable and consistent rankings while offering greater flexibility for handling complex uncertainty through the CPyFS framework.
Toward Smarter and More Reliable Mining Decisions
Modern underground coal mines generate large amounts of technical, operational, and safety-related information. Converting this data into effective operational decisions remains a major challenge.
This study demonstrates that combining objective weighting methods with advanced fuzzy decision-making techniques could improve the way mining companies evaluate complex operational strategies. The practical value of this combination remains to be established in real-world conditions, however.
The proposed CRITIC–WASPAS framework could provide mine managers with a structured and transparent decision-support tool. The framework helps prioritize strategies that balance productivity with worker protection by simultaneously considering safety, operational efficiency, maintenance, technology adoption, and leadership commitment. Its ability to objectively evaluate competing alternatives also reduces reliance on subjective judgment during strategic planning.
Although the study used hypothetical data based on realistic mining scenarios, the results highlight the framework's strong methodological potential. Future research should validate the approach using real operational data from active underground coal mines. Additional studies could also integrate other aggregation operators and decision-making techniques to further improve the model's flexibility and performance.
Overall, the research demonstrates that advanced fuzzy decision-making methods offer a promising foundation to support safer, more efficient, and more consistent mining operations. As underground mines become increasingly automated and data-driven, decision-support frameworks such as CRITIC–WASPAS can help mining companies make better operational decisions under uncertainty.
Journal Reference
Cui, J. (2026). Risk management and operational efficiency in underground coal mining using circular Pythagorean fuzzy CRITIC WASPAS approach with prioritized weights. Scientific Reports. https://www.nature.com/articles/s41598-026-57856-w.
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