DSS MEREC

Languages: English | Bahasa Indonesia

English

Implementation of MEREC (Method based on Removal Effects of Criteria) algorithm for calculating criteria weights in multi-criteria decision making systems.

Overview

MEREC (Method based on the Removal Effects of Criteria) is a modern multi-criteria decision-making (MCDM) method that determines criteria weights based on the removal effects of each criterion on the overall performance of alternatives.

Important: MEREC is only for calculating criteria weights, not for ranking alternatives. Use the generated weights for other MCDM methods like TOPSIS, SAW, or WASPAS.

Features

• Pure MEREC Implementation: Clean implementation of MEREC algorithm for weighting
• TypeScript Support: Full TypeScript support with comprehensive type definitions
• Well Tested: Comprehensive test suite with various edge cases
• Easy to Use: Simple API with sensible defaults
• Configurable: Customizable options for different use cases
• Well Documented: Complete documentation with examples

Installation

npm install dss-merec

Quick Start

CommonJS (Node.js):

const { Merec } = require("dss-merec");

// Define your decision matrix
const matrix = [
  [8, 7, 6, 5], // Alternative 1
  [6, 8, 7, 6], // Alternative 2
  [7, 6, 8, 7], // Alternative 3
  [5, 9, 5, 8], // Alternative 4
];

// Define criteria types
const criteriaTypes = ["benefit", "benefit", "cost", "benefit"];

// Calculate criteria weights
const weights = Merec.CalculateWeight(matrix, criteriaTypes);

console.log("Criteria weights:", weights);
// Output: [0.3519, 0.1917, 0.1998, 0.2566]

ES Modules (TypeScript/Modern JS):

import { Merec } from "dss-merec";

// Define your decision matrix
const matrix = [
  [8, 7, 6, 5], // Alternative 1
  [6, 8, 7, 6], // Alternative 2
  [7, 6, 8, 7], // Alternative 3
  [5, 9, 5, 8], // Alternative 4
];

// Define criteria types
const criteriaTypes: ("benefit" | "cost")[] = [
  "benefit",
  "benefit",
  "cost",
  "benefit",
];

// Calculate criteria weights
const weights = Merec.CalculateWeight(matrix, criteriaTypes);

console.log("Criteria weights:", weights);
// Output: [0.3519, 0.1917, 0.1998, 0.2566]

API Reference

Static Method

Merec.CalculateWeight(matrix, criteriaTypes)

Calculate criteria weights using MEREC algorithm.

Parameters:

• matrix (number[][]): Decision matrix where rows = alternatives, columns = criteria
• criteriaTypes (("benefit" | "cost")[]): Array of criteria types

Returns:

• number[]: Array of criteria weights [0-1] that sum to 1.0

Example:

const matrix = [
  [4.5, 25, 150000], // Alternative 1: [rating, reviews, price]
  [3.8, 65, 72000], // Alternative 2
  [4.2, 95, 88000], // Alternative 3
];

const criteriaTypes = ["benefit", "benefit", "cost"];
const weights = Merec.CalculateWeight(matrix, criteriaTypes);

Criteria Types

• "benefit": Higher values are better (e.g., quality, performance, rating)
• "cost": Lower values are better (e.g., price, distance, time)

Input Validation

The method includes comprehensive input validation:

try {
  const weights = Merec.CalculateWeight(matrix, criteriaTypes);
} catch (error) {
  console.error("Error:", error.message);
  // Possible errors:
  // - "Matrix tidak boleh kosong" (Empty matrix)
  // - "Tipe kriteria tidak boleh kosong" (Empty criteria types)
  // - "Jumlah kolom matrix (X) harus sama dengan jumlah tipe kriteria (Y)"
  // - "Baris X harus memiliki Y kolom" (Inconsistent row lengths)
}

Algorithm Details

The MEREC algorithm follows these steps:

1. Decision Matrix Creation: Organize alternatives and criteria into a matrix
2. Normalization: Apply MEREC-specific normalization
   • Benefit criteria: n_ij = x_ij / max(x_j)
   • Cost criteria: n_ij = min(x_j) / x_ij
3. Overall Performance: Calculate S_i = ln(1 + (1/m) * Σ|ln(n_ij)|)
4. Removal Effects: Calculate performance without each criterion
5. Deviation Calculation: Measure impact of removing each criterion
6. Weight Determination: Calculate weights based on removal effects

Output: Criteria weights array that can be used for other MCDM methods.

Performance Considerations

• Time Complexity: O(m×n²) where m = alternatives, n = criteria
• Space Complexity: O(m×n)
• Recommended Limits: < 1000 alternatives, < 50 criteria for optimal performance

Integration with Other MCDM Methods

Use MEREC weights with other decision-making methods:

// Calculate weights with MEREC
const weights = Merec.CalculateWeight(matrix, criteriaTypes);

// Use with TOPSIS
const topsisResult = topsis(matrix, weights, criteriaTypes);

// Use with SAW (Simple Additive Weighting)
const sawResult = saw(matrix, weights, criteriaTypes);

// Use with WASPAS
const waspasResult = waspas(matrix, weights, criteriaTypes);

Bahasa Indonesia

Implementasi algoritma MEREC (Method based on Removal Effects of Criteria) untuk menghitung bobot kriteria dalam sistem pengambilan keputusan multi-kriteria.

Gambaran Umum

MEREC (Method based on the Removal Effects of Criteria) adalah metode pengambilan keputusan multi-kriteria modern yang menentukan bobot kriteria berdasarkan efek penghapusan setiap kriteria terhadap kinerja keseluruhan alternatif.

Penting: MEREC hanya untuk menghitung bobot kriteria, bukan untuk ranking alternatif. Gunakan bobot yang dihasilkan untuk metode MCDM lain seperti TOPSIS, SAW, atau WASPAS.

Fitur

• Implementasi MEREC Murni: Implementasi bersih algoritma MEREC untuk pembobotan
• Dukungan TypeScript: Dukungan penuh TypeScript dengan definisi tipe lengkap
• Teruji dengan Baik: Test suite komprehensif dengan berbagai edge case
• Mudah Digunakan: API sederhana dengan pengaturan default yang masuk akal
• Dapat Dikonfigurasi: Opsi yang dapat disesuaikan untuk berbagai use case
• Dokumentasi Lengkap: Dokumentasi lengkap dengan contoh-contoh

Instalasi

npm install dss-merec

Memulai Cepat

CommonJS (Node.js):

const { Merec } = require("dss-merec");

// Definisikan matriks keputusan Anda
const matrix = [
  [8, 7, 6, 5], // Alternatif 1
  [6, 8, 7, 6], // Alternatif 2
  [7, 6, 8, 7], // Alternatif 3
  [5, 9, 5, 8], // Alternatif 4
];

// Definisikan tipe kriteria
const criteriaTypes = ["benefit", "benefit", "cost", "benefit"];

// Hitung bobot kriteria
const weights = Merec.CalculateWeight(matrix, criteriaTypes);

console.log("Bobot kriteria:", weights);
// Output: [0.3519, 0.1917, 0.1998, 0.2566]

ES Modules (TypeScript/Modern JS):

import { Merec } from "dss-merec";

// Definisikan matriks keputusan Anda
const matrix = [
  [8, 7, 6, 5], // Alternatif 1
  [6, 8, 7, 6], // Alternatif 2
  [7, 6, 8, 7], // Alternatif 3
  [5, 9, 5, 8], // Alternatif 4
];

// Definisikan tipe kriteria
const criteriaTypes: ("benefit" | "cost")[] = [
  "benefit",
  "benefit",
  "cost",
  "benefit",
];

// Hitung bobot kriteria
const weights = Merec.CalculateWeight(matrix, criteriaTypes);

console.log("Bobot kriteria:", weights);
// Output: [0.3519, 0.1917, 0.1998, 0.2566]

Referensi API

Method Static

Merec.CalculateWeight(matrix, criteriaTypes)

Menghitung bobot kriteria menggunakan algoritma MEREC.

Parameter:

• matrix (number[][]): Matriks keputusan dimana baris = alternatif, kolom = kriteria
• criteriaTypes (("benefit" | "cost")[]): Array tipe kriteria

Returns:

• number[]: Array bobot kriteria [0-1] yang berjumlah 1.0

Contoh:

const matrix = [
  [4.5, 25, 150000], // Alternatif 1: [rating, review, harga]
  [3.8, 65, 72000], // Alternatif 2
  [4.2, 95, 88000], // Alternatif 3
];

const criteriaTypes = ["benefit", "benefit", "cost"];
const weights = Merec.CalculateWeight(matrix, criteriaTypes);

Tipe Kriteria

• "benefit": Nilai lebih tinggi lebih baik (contoh: kualitas, performa, rating)
• "cost": Nilai lebih rendah lebih baik (contoh: harga, jarak, waktu)

Validasi Input

Method ini menyertakan validasi input yang komprehensif:

try {
  const weights = Merec.CalculateWeight(matrix, criteriaTypes);
} catch (error) {
  console.error("Error:", error.message);
  // Kemungkinan error:
  // - "Matrix tidak boleh kosong"
  // - "Tipe kriteria tidak boleh kosong"
  // - "Jumlah kolom matrix (X) harus sama dengan jumlah tipe kriteria (Y)"
  // - "Baris X harus memiliki Y kolom"
}

Detail Algoritma

Algoritma MEREC mengikuti langkah-langkah berikut:

1. Pembuatan Matriks Keputusan: Mengorganisir alternatif dan kriteria ke dalam matriks
2. Normalisasi: Menerapkan normalisasi spesifik MEREC
   • Kriteria benefit: n_ij = x_ij / max(x_j)
   • Kriteria cost: n_ij = min(x_j) / x_ij
3. Kinerja Keseluruhan: Menghitung S_i = ln(1 + (1/m) * Σ|ln(n_ij)|)
4. Efek Penghapusan: Menghitung kinerja tanpa setiap kriteria
5. Perhitungan Deviasi: Mengukur dampak penghapusan setiap kriteria
6. Penentuan Bobot: Menghitung bobot berdasarkan efek penghapusan

Output: Array bobot kriteria yang dapat digunakan untuk metode MCDM lain.

Integrasi dengan Metode MCDM Lain

Gunakan bobot MEREC dengan metode pengambilan keputusan lain:

// Hitung bobot dengan MEREC
const weights = Merec.CalculateWeight(matrix, criteriaTypes);

// Gunakan dengan TOPSIS
const topsisResult = topsis(matrix, weights, criteriaTypes);

// Gunakan dengan SAW (Simple Additive Weighting)
const sawResult = saw(matrix, weights, criteriaTypes);

// Gunakan dengan WASPAS
const waspasResult = waspas(matrix, weights, criteriaTypes);

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this library in academic research, please cite:

Keshavarz-Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015).
Multi-criteria inventory classification using a new method of evaluation based on removal effects of criteria (MEREC).
Informatica, 26(4), 615-632.

Support

If you encounter any issues or have questions, please file an issue on the GitHub repository.