A bipolar triangular fuzzy ARLON based decision support system for optimal CNC machine tool selection in smart...

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.