AHP-CALC

A TypeScript module implementing the Analytical Hierarchy Process (AHP) for decision support systems.
It supports pairwise comparison matrices, calculates priority weights, and evaluates consistency.
Ideal for multi-criteria decision analysis (MCDA).

⚠️ Note: This library does not support sub-criteria (yet).

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📋 Requirements

This library requires:

• A pairwise comparison matrix for the criteria.
• A pairwise comparison matrix for the alternatives with respect to each criterion.

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📦 Installation

npm install ahp-calc

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Documentation

Documentation

Main Feature

• Support for Pairwise Comparison Matrices: The library allows you to create and process pairwise comparison matrices for both criteria and alternatives.

• Matrix Normalization: Supports normalization of both criteria and alternatives matrices to calculate relative priorities.

• Weight Calculation (bobot priority): It calculates local priority weights (eigenvectors) for criteria and alternatives using the normalized matrices, and it can compute the global weights across all levels.

• Consistency Evaluation: Includes functionality for consistency checking using λmax (Lamda Max), Consistency Index (CI), and Consistency Ratio (CR) to evaluate whether the matrix comparisons are consistent.

• Converts Nested String Matrices to Numeric Matrices: The library supports converting string-based matrices (nested) into numeric matrices, making it easier to process data in AHP.

• Priority Weights Calculation for Alternatives: For multi-alternative problems, the library supports calculating priority weights for alternatives against each criterion.

• Consistency Check for Alternatives: The library can evaluate the consistency of matrices for alternatives as well, using the same λmax, CI, and CR methods.

No Support for Sub-criteria: ⚠️ Currently, the library does not support sub-criteria for further hierarchical decision-making.

Example Usage

you can start directly using function to calculate the pairwaise

for example to calculate Criteria Pairwise Comparison Matrices:

import { calculcateCritMatrix } from "ahp-calc";

const critMatriks: string[][] = [
  ["1", "3", "5", "7", "7"],
  ["1/3", "1", "3", "5", "5"],
  ["1/5", "1/3", "1", "3", "3"],
  ["1/7", "1/5", "1/3", "1", "2"],
  ["1/7", "1/5", "1/3", "1/2", "1"],
];

const {
  normalizedMatrix,
  CI,
  CR,
  RI,
  originalMatrix,
  konsistensi,
  lamdaMax,
  sumCrit,
  n,
  weightsCriteria,
} = calculcateCritMatrix(critMatriks);
console.log({
  sumCrit,
  normalizedMatrix,
  CI,
  CR,
  RI,
  originalMatrix,
  lamdaMax,
  konsistensi,
  n,
  weightsCriteria,
});
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for example to calculate alternatif Pairwise Comparison Matrices and if you just wan to take normalize you can do like this:

import { calculateAltMatrix } from "ahp-calc";
const altMatrix: string[][][] = [
  [
    ["1", "3", "2", "1/3"],
    ["1/3", "1", "1/3", "1/5"],
    ["1/2", "3", "1", "1/3"],
    ["3", "5", "3", "1"],
  ],
  [
    ["1", "3", "5", "2"],
    ["1/3", "1", "3", "1/3"],
    ["1/5", "1/3", "1", "1/5"],
    ["1/2", "3", "5", "1"],
  ],
  [
    ["1", "1/3", "1/5", "1/5"],
    ["3", "1", "1/3", "1/3"],
    ["5", "3", "1", "2"],
    ["5", "3", "1/2", "1"],
  ],
  [
    ["1", "1/3", "2", "1/3"],
    ["3", "1", "3", "2"],
    ["1/2", "1/3", "1", "1/3"],
    ["3", "1/2", "3", "1"],
  ],
];

const { normalized } = calculateAltMatrix(altMatrix);

console.log(normalized);
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Or you can start using the library by first creating an instance of the AHPCrit or AHPAlt class depending on whether you're working with criteria or alternatives.

import { AHPCrit } from "ahp-calc";

const critMatrix = [
  [
    [1.0, 3.0, 0.2],
    [0.3333, 1.0, 0.1429],
    [5.0, 7.0, 1.0],
  ],
];

const weights = AHPCrit.calculateCriteriaWeight(critMatrix);
console.log(weights);
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To count each column in all matriks for alternatives:

import { AHPAlt } from "ahp-calc";

const originalMatrix = [
  [
    [1.0, 3.0, 0.5],
    [0.3333, 1.0, 0.1429],
    [2.0, 7.0, 1.0],
  ],
  [
    [1.0, 3.0, 0.5],
    [0.3333, 1.0, 0.1429],
    [2.0, 7.0, 1.0],
  ],
  [
    [1.0, 3.0, 0.5],
    [0.3333, 1.0, 0.1429],
    [2.0, 7.0, 1.0],
  ],
];

const sumAlt = AHPAlt.countTotalAlterEachColumn(originalMatrix);
console.log(sumAlt);
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Ranks alternatif

const { weightsCriteria } = calculcateCritMatrix(critMatriks);
const { weightAlt } = calculateAltMatrix(altMatrix);
const res = calculateCompositeWeights(weightAlt, weightsCriteria);
console.log(res);
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