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Attribute-based characterization, coding, and selection of joining processes using a novel MADM approach

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  • Ravindra Singh Saluja

    (BITS Pilani)

  • Varinder Singh

    (BITS Pilani)

Abstract

This article aims to present a systematic and robust approach for joining process selection. The proposed approach identifies important attributes for this decision based on a literature survey. It is demonstrated that for most of the commercially available joining process choices, the specifications available from the suppliers of process equipment do not address all decision concerns. An approach to codifying the required specification information is suggested to aid in initiating the selection process. The demonstrated selection process consists of searching alternatives based on meeting the threshold of a set of critical attributes and then evaluation of choices by a novel multi-attribute decision-making (MADM) procedure, which is developed by hybridization of the Best-Worst Method (BWM), Sample Variance Analogy (SVA) and Multi-Objective Optimization on the basis of Ratio Analysis plus full Multiplicative form (MULTIMOORA) and is named as BWM-SVA-MULTIMOORA approach. The approach is demonstrated by an example of joining process selection for stainless steel-based components in a nuclear plant. Further, rank reversal and sensitivity analysis has been performed to demonstrate the robustness and stability of the ranking obtained.

Suggested Citation

  • Ravindra Singh Saluja & Varinder Singh, 2023. "Attribute-based characterization, coding, and selection of joining processes using a novel MADM approach," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 616-655, June.
    • Handle: RePEc:spr:opsear:v:60:y:2023:i:2:d:10.1007_s12597-023-00623-6
    DOI: 10.1007/s12597-023-00623-6
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    References listed on IDEAS

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    6. Manoj Mathew & Santosh Kumar Patanwar & Sanjay Gupta & Taranjeet Sachdev, 2021. "Performance evaluation of arc welding processes for the manufacturing of pressure vessel using novel hybrid MCDM technique," International Journal of Information and Decision Sciences, Inderscience Enterprises Ltd, vol. 13(3), pages 235-253.
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