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A bipolar triangular fuzzy ARLON based decision support system for optimal CNC machine tool selection in smart
Ubaid Ur Rehman1, Meraj Ali Khan2, Ibrahim Al-Dayel2
1Department of Mathematics, University of Management and Technology, Johar Town, Lahore, C-II, 54700, Punjab, Pakistan.
Scientific Reports
|July 16, 2026
Summary
Selecting the right Computer Numerical Control (CNC) machine is vital for manufacturing efficiency. The novel bipolar triangular fuzzy alternative ranking using a two-step logarithmic normalization (BTF-ARLON) model offers superior decision support for complex machine selection scenarios.
Area of Science:
- Manufacturing Engineering
- Operations Research
- Decision Science
Background:
- Computer Numerical Control (CNC) machines are critical in modern manufacturing, impacting precision, costs, and overall efficacy.
- Traditional multi-criteria decision-making methods like classical and fuzzy ARLON inadequately handle uncertainty, bipolar preferences, and linguistic patterns.
- Existing methods struggle to integrate bipolarity, triangular fuzzy logic, and diverse evaluation criteria simultaneously.
Purpose of the Study:
- To introduce a superior bipolar triangular fuzzy alternative ranking using a two-step logarithmic normalization (BTF-ARLON) model.
- To provide a systematic framework for assessing and prioritizing CNC machines amidst conflicting technical and economic factors.
- To enhance decision-making in complex manufacturing environments by addressing limitations of traditional methods.
Main Methods:
- Development of the Bipolar Triangular Fuzzy Alternative Ranking using a Two-step Logarithmic Normalization (BTF-ARLON) model.
- Application of the BTF-ARLON model in a case study involving five CNC machine options evaluated on nine key performance indicators.
- Calculation of superiority and inferiority flows to generate intuitionistic fuzzy balanced rankings.
Main Results:
- The BTF-ARLON model successfully integrates uncertainty, bipolar reasoning, and triangular fuzziness within a single decision-support framework.
- Comparative analysis shows BTF-ARLON outperforms classical ARLON, fuzzy ARLON, and intuitionistic fuzzy ARLON in handling complex decision properties.
- Empirical studies demonstrate that BTF-ARLON yields more consistent, discriminative, and practically significant results for CNC machine selection.
Conclusions:
- The BTF-ARLON model is a potent and comprehensive instrument for CNC machine selection and assessment in complex manufacturing settings.
- This novel approach effectively addresses the methodological shortcomings of traditional multi-criteria decision-making techniques.
- The framework provides robust and interpretable rankings, enhancing the efficacy of manufacturing decision-making processes.
