A Novel Symmetric Anchor Scoring (SAS) Method for Industrial Location Selection: Application to Electric Vehicle Manufacturing in Tamil Nadu, India

Year : 2026 | Volume : 04 | Issue : 01 | Page : 8 19
    By

    S. Mahalakshmi,

  • V. Sathiya,

  • S. Ramabalan,

  • N. Godwin Raja Ebenezer,

  1. Professor, Department of Information Technology, Selvam College of Technology, Namakkal, Tamil Nadu, India
  2. Professor, Department of Computer Science and Engineering, Selvam College of Technology, Namakkal, Tamil Nadu, India
  3. Professor, Department of Mechanical Engineering, Selvam College of Technology, Namakkal, Tamil Nadu, India
  4. Professor, Department of Mechanical Engg., MAM College of Engineering, Trichy, Tamil Nadu, India

Abstract

This work highlights the city-level detail of decision-making enabled by a new MCDM method Symmetry Anchor Scoring (SAS). Generally conventional methods are good at selecting one best and one worst alternative, but not differentiating among the many strong contenders more likely in practice. Linking MCDM methods to the tradition of decision making, the paper discusses the mathematics and analogical basis of SAS design, provides a visual proof of its ideal point symmetry, and illustrates how alternative scoring compromises SAS optimality. SAS innovatively adopts mathematical symmetry as the basis for atypical reference subset comparators. No traditional concept is borrowed from either optimization or comparison frameworks. SAS offshore wind farms locate and the electric vehicle (EV) manufacturing location selection embedded case comparisons demonstrate its performance. Comparing SAS to a leading MCDM method, this paper concludes by recommending areas for application to other decision-making challenges and potential future research strengths. Comparison with AHP-TOPSIS shows that the top ranking alternatives have fairly high levels of consensus. Additionally, through a thorough sensitivity analysis, it is shown that SAS outperforms AHP-TOPSIS in terms of maintaining preference stability over a range of priority scenarios. The SAS solution provides a simple, intuitive, transparent, and weight-free MCDM method when compared to traditional techniques. This will be attractive to decision-makers in industry who are faced with making complex, ill-defined planning decisions, involving multiple competing objectives, and where priorities are uncertain. It was designed for ease of implementation and requires less computational time compared to conventional methods. Additionally, transparency is facilitated by the symmetric tripartite form, a common preference needed to be present for the selected alternative to be ranked among the TOPSIS flight options being compared.

Keywords: Novel MCDM method, multi-criteria decision making, symmetric anchor scoring, electric vehicle manufacturing, industrial location selection, sensitivity analysis, Tamil Nadu, automotive industry

[This article belongs to International Journal of Industrial and Product Design Engineering ]

How to cite this article:
S. Mahalakshmi, V. Sathiya, S. Ramabalan, N. Godwin Raja Ebenezer. A Novel Symmetric Anchor Scoring (SAS) Method for Industrial Location Selection: Application to Electric Vehicle Manufacturing in Tamil Nadu, India. International Journal of Industrial and Product Design Engineering. 2026; 04(01):8-19.
How to cite this URL:
S. Mahalakshmi, V. Sathiya, S. Ramabalan, N. Godwin Raja Ebenezer. A Novel Symmetric Anchor Scoring (SAS) Method for Industrial Location Selection: Application to Electric Vehicle Manufacturing in Tamil Nadu, India. International Journal of Industrial and Product Design Engineering. 2026; 04(01):8-19. Available from: https://journals.stmjournals.com/ijipde/article=2026/view=239650


References

1. AICTE. (2022). Technical education institute database. All India Council for Technical Education.
2. Belton, V., & Stewart, T. J. (2002). Multiple criteria decision analysis: An integrated approach. Springer.
3. Brown, P. A., & Gibson, D. F. (1972). A quantified model for facility site selection. Journal of Marketing, 36(2), 32–38.
4. Celen, A. (2014). Comparative analysis of normalization procedures in TOPSIS method. Informatica, 25(2), 185–208.
5. CGWB. (2022). Dynamic ground water resources of Tamil Nadu. Central Ground Water Board.
6. Chakraborty, S., et al. (2021). Hybrid MCDM approaches for industrial site selection. Expert Systems with Applications, 185, 115608.
7. Guidance Tamil Nadu. (2023). Investment opportunities in Tamil Nadu. Government of Tamil Nadu.
8. Gupta, R., et al. (2022). Location selection for electronics manufacturing corridors in India. International Journal of Production Economics, 244, 108376.
9. Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Springer-Verlag.
10. ICAR. (2021). Climate vulnerability assessment for Tamil Nadu. Indian Council of Agricultural Research.
11. Ishizaka, A., & Labib, A. (2011). Review of the main developments in the analytic hierarchy process. Expert Systems with Applications, 38(11), 14336–14345.
12. Jahan, A., & Edwards, K. L. (2015). Influence of normalization techniques in ranking. Materials & Design, 65, 335–342.
13. Kathuria, S. (2019). The making of an automotive cluster in Tamil Nadu. World Bank.
14. Kumar, M., et al. (2022). Sustainable location selection for electric vehicle manufacturing. Journal of Cleaner Production, 356, 131824.
15. Mendoza, G. A., & Martins, H. (2006). Multi-criteria decision analysis in natural resource management. Forest Ecology and Management, 230(1–3), 1–22.
16. NHAI. (2022). Highway connectivity report for industrial corridors. National Highways Authority of India.
17. NITI Aayog. (2021). India’s electric mobility transformation. Government of India.
18. NSDC. (2022). Skill gap study for automotive sector. National Skill Development Corporation.
19. Razmi, J., et al. (2018). Industrial zone selection using fuzzy TOPSIS. International Journal of Advanced Manufacturing Technology, 94(1–4), 803–818.
20. Roy, B. (1991). The outranking approach and the foundations of ELECTRE methods. Theory and Decision, 31(1), 49–73.
21. Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill.
22. Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98.
23. Sharma, S., et al. (2021). Pharmaceutical cluster location selection in India. Operations Research Perspectives, 8, 100185.
24. Shekhovtsov, A., & Kołodziejczyk, J. (2020). Do distance-based MCDM methods create similar rankings? Procedia Computer Science, 176, 3718–3729.
25. SIPCOT. (2023). Industrial park master plans. State Industries Promotion Corporation of Tamil Nadu.
26. Singh, A., et al. (2020). Renewable energy plant location selection in India. Renewable Energy, 151, 343–353.
27. TANGEDCO. (2022). Industrial power supply reliability report. Tamil Nadu Generation and Distribution Corporation.
28. TIDCO. (2023). Tamil Nadu industrial development report. Tamil Nadu Industrial Development Corporation.
29. TNPCB. (2022). Environmental compliance report. Tamil Nadu Pollution Control Board.
30. Tuzkaya, G., et al. (2010). An analytic network process approach for locating undesirable facilities. Journal of Hazardous Materials, 176(1-3), 1143–1154.
31. World Bank. (2022). Doing business in India. World Bank Group.
32. Zhou, P., et al. (2020). Industrial location selection: A literature review. Sustainable Cities and Society, 61, 102283.

Regular Issue Subscription Original Research
Volume 04
Issue 01
Received 03/02/2026
Accepted 24/02/2026
Published 16/03/2026
Publication Time 41 Days
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