Skip to contentScholarGate
LibraryBookshelfDeskReview StudioAssistant
Sign in
On this page
IntuitionHow it worksWhen to use itStrengths & limitationsCommon pitfalls🔒 Read the full methodSourcesRelated methods
Cite this pageSpotted an issue on this page? Report or suggest a fix →
Home›Decision-making›COPRAS-IN — COPRAS with Interval Neutrosophic Numbers (INN)
MCDMRankingInterval neutrosophic

COPRAS-IN — COPRAS with Interval Neutrosophic Numbers (INN)

N-COPRAS (COPRAS-IN — COPRAS with Interval Neutrosophic Numbers (INN)) is a ranking multi-criteria decision-making (MCDM) method introduced by Şahin, R. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

ScholarGate
  1. MCDM
  2. v1
  3. 1 Sources
  4. PUBLISHED
Cite this page →
Tools & resources
Download slides
Learn & explore

Read the full method

Members only

Sign in with a free account to read this section.

Sign in

Method map

The neighbourhood of related methods — select a node to explore.

N-COPRAS
AHPANPBWMCOPRASCRITICENTROPYMEREC

When to use it

COPRAS-IN extends the crisp COPRAS method to Interval Neutrosophic Numbers (INN). Each criterion value is expressed as <[T_L,T_U],[I_L,I_U],[F_L,F_U]>. Cost and benefit criteria are separated and aggregated via INWMSM operators. The risk index λ controls decision-maker attitude: λ=0 is pessimistic (focuses on indeterminacy/falsity), λ=1 is optimistic (focuses on truth-membership). The alternative with the highest Q_i score is best.

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
  • May exhibit rank reversal when alternatives are added to or removed from the set.
  • Assumes full compensation — a strong score on one criterion can offset a weak score on another.

Sources

  1. Şahin, R. (2019). COPRAS Method with Neutrosophic Sets. Fuzzy Multi-criteria Decision-Making Using Neutrosophic Sets, Studies in Fuzziness and Soft Computing, vol 369, Springer, Cham DOI: 10.1007/978-3-030-00045-5_19 ↗

How to cite this page

ScholarGate. (2026, June 2). COPRAS-IN — COPRAS with Interval Neutrosophic Numbers (INN). ScholarGate. https://scholargate.app/en/decision-making/n-copras

Related methods

AHPANPBWMCOPRASCRITICENTROPYMEREC

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.

  • AHPDecision-making↔ compare
  • ANPDecision-making↔ compare
  • BWMDecision-making↔ compare
  • COPRASDecision-making↔ compare
  • CRITICDecision-making↔ compare
  • ENTROPYDecision-making↔ compare
  • MERECDecision-making↔ compare
Compare side by side →

Similar methods

QR-COPRASIVIF-COPRASROUGH-COPRASP-COPRASIF-COPRASFUZZY-COPRASN-WASPASIV-COPRAS

Related reference concepts

Decision MakingDecision Support SystemsWeighted ScoresEvaluation CriteriaQ MethodologyDecision Making Skills

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

ScholarGate — N-COPRAS (COPRAS-IN — COPRAS with Interval Neutrosophic Numbers (INN)). Retrieved 2026-07-21 from https://scholargate.app/en/decision-making/n-copras · Dataset: https://doi.org/10.5281/zenodo.20539026
Quick facts
Originator
Şahin, R.
Subfamily
Ranking
Year
2019
Type
Interval Neutrosophic ranking — INN: <[T L,T U],[I L,I U],[F L,F U]> with 0 ≤ T U+I U+F U ≤ 3
Value Space
Interval neutrosophic
Uncertainty
hybrid
Compensation
full
Rank Reversal
Yes
Related methods
AHPANPBWMCOPRASCRITICENTROPYMEREC
ScholarGate

A content-first reference library for research methods — what each one is, how it works, and where it comes from.

Open data (CC-BY)

Explore

  • Library
  • Search the library…
  • Browse by field
  • Fields
  • Journey
  • Compare
  • Which method?

Reference

  • Subjects
  • Atlas
  • Glossary
  • Methodology
  • Philosophy

Your tools

  • Bookshelf
  • Desk
  • Chat

Company

  • About
  • Pricing
  • Contact
  • Suggest a method

Entries are compiled from published sources for reference. Verifying the accuracy and suitability of any information for your own use remains your responsibility.

© 2026 ScholarGate · A research-method reference library
  • Privacy
  • Cookies
  • Terms
  • Delete account