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Home›Decision-making›Simple Additive Weighting
MCDMRankingcrisp

Simple Additive Weighting

SAW (Simple Additive Weighting) is a ranking multi-criteria decision-making (MCDM) method introduced by Fishburn, P. C. in 1967. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

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  1. MCDM
  2. v1
  3. 1 Sources
  4. PUBLISHED
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SAW
AHPANPBWMBWM-BAYESIANCCSDCILOSCIMASCRITICData-Driven MCDAFUZZY-SAW

+5 more

When to use it

S_i ∈ [0,1] after linear-max normalisation (assuming all positive values). Higher S_i means better overall performance. SAW is fully compensatory — a very high value on one criterion can offset a low value on another.

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
  • Assumes full compensation — a strong score on one criterion can offset a weak score on another.

Sources

  1. Fishburn, P. C. (1967). Additive utilities with incomplete product sets: Application to priorities and assignments. Operations Research DOI: 10.1287/opre.15.3.537 ↗

How to cite this page

ScholarGate. (2026, June 2). Simple Additive Weighting. ScholarGate. https://scholargate.app/en/decision-making/saw

Related methods

AHPANPBWMBWM-BAYESIANCCSDCILOSCIMASCRITIC

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

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Referenced by

Data-Driven MCDAFUZZY-SAWMIN-MAX-NORMALIZATIONPIF-SAWPSISAPEVO-MSECA

Similar methods

WSMGREY-SAWFUZZY-SAWIV-SAWIF-SAWPF-SAWPIF-SAWWASPAS

Related reference concepts

Weighted ScoresDecision MakingDecision Support SystemsEvaluation CriteriaParticipative Decision MakingPrincipal Component Analysis

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

ScholarGate — SAW (Simple Additive Weighting). Retrieved 2026-07-20 from https://scholargate.app/en/decision-making/saw · Dataset: https://doi.org/10.5281/zenodo.20539026
Quick facts
Originator
Fishburn, P. C.
Subfamily
Ranking
Year
1967
Type
Additive utility (linear)
Value Space
crisp
Uncertainty
None
Compensation
full
Rank Reversal
No
Related methods
AHPANPBWMBWM-BAYESIANCCSDCILOSCIMASCRITIC
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