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Home›Decision-making›Proximity Indexed Value
MCDMRankingcrisp

Proximity Indexed Value

PIV (Proximity Indexed Value) is a ranking multi-criteria decision-making (MCDM) method introduced by Mufazzal, S. Muzakkir, S. M. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

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When to use it

PIV uses absolute differences from ideal (not squared); more resistant to rank reversal than TOPSIS.

Strengths & limitations

Strengths
  • Follows a transparent, reproducible computational procedure that can be audited step by step.
  • Handles multiple criteria of differing scales and units within a single decision matrix.
Limitations
  • Results depend on the chosen normalisation, weights, and parameter settings.

Sources

  1. Mufazzal, S., Muzakkir, S. M. (2018). A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals. Computers & Industrial Engineering DOI: 10.1016/j.cie.2018.03.045 ↗

How to cite this page

ScholarGate. (2026, June 2). Proximity Indexed Value. ScholarGate. https://scholargate.app/en/decision-making/piv

Similar methods

PIF-TODIMPF-VIKORPF-TOPSISIV-TOPSISPIF-TOPSISPROBIDCUBIC-TOPSISPIPRECIA

Related reference concepts

Decision MakingDecision Support SystemsWeighted ScoresDecision Making SkillsEvaluation CriteriaMultidimensional Scaling

Spotted an issue on this page? Report or suggest a fix →

ScholarGate — PIV (Proximity Indexed Value). Retrieved 2026-07-21 from https://scholargate.app/en/decision-making/piv · Dataset: https://doi.org/10.5281/zenodo.20539026
Quick facts
Originator
Mufazzal, S. Muzakkir, S. M.
Subfamily
Ranking
Year
2018
Type
Distance from weighted ideal minimization via linear proximity index
Value Space
crisp
Uncertainty
None
Compensation
partial
Rank Reversal
No
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