flutter_decision_making 1.0.1

Dart package for lightweight decision making using the Analytic Hierarchy Process (AHP). Helps prioritize alternatives objectively via pairwise comparison.

A Flutter package for implementing multi-criteria decision-making using the Analytic Hierarchy Process (AHP).

Easily manage criteria, alternatives, pairwise comparisons, consistency checks, and final decision scoring — all in one unified package.

🧠 This package implements the Analytic Hierarchy Process (AHP) originally developed by Thomas L. Saaty.

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✨ Features

• 🏗 Generate hierarchy from criteria and alternatives
• ⚖️ Pairwise comparisons using Saaty's 1–9 scale
• ✅ Consistency Ratio check to ensure logical consistency
• 📊 Eigenvector and final score calculation
• 🔧 Customizable and extendable architecture
• 🛠 Built-in performance profiling (dev-friendly)

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🚀 Getting Started

Initialize the FlutterDecisionMaking instance before using other methods.

late FlutterDecisionMaking _decisionMaking;

 @override
  void initState() {
    super.initState();
    _decisionMaking = FlutterDecisionMaking();

📚 Usage Guide

🛠️ User Role

1. 🧱 Define Criteria and Alternatives

Each item must have a unique ID. If not provided, the package auto-generates it.

final criteria = [
  Criteria(id: 'c1', name: 'Price'),
  Criteria(id: 'c2', name: 'Quality'),
];

final alternatives = [
  Alternative(id: 'a1', name: 'Product A'),
  Alternative(id: 'a2', name: 'Product B'),
];

2. 🧮 Identification, Generate Hierarchy and Generate Pairwise Comparison Input

Validate and prepare your inputs.

await _decisionMaking.generateHierarchyAndPairwiseTemplate(
listCriteria: criteria,listAlternative: alternatives);

After the process is complete, a paired matrix list will be generated:

List<PairwiseComparisonInput<Criteria>> inputCriteria;

List<PairwiseAlternativeInput> inputAlternative;

Please ensure that all priority weights are filled in before proceeding to the next step.

3. 🔁 Generate Result

Call this method to compute the final scores based on input data.

await _decisionMaking.generateResult()

🛠️ AHP Role

on generate result, AHP will do

1. 🧮 Generate Pairwise Matrix

Create a pairwise comparison matrix from criteria or alternatives input.

2. 🧮 Calculate Approximate Eigenvector

Calculate priority weights (eigenvector) from the pairwise matrix.

3. 🧮 Check Consistency Ratio

Compute and verify matrix consistency using the Consistency Ratio (CR).

4. 🧮 Calculate Result

Calculate final scores for alternatives based on criteria and alternative weights, including consistency checks.

The result includes:

• Sorted list of alternative scores (results)
• Consistency status and ratio for criteria and alternatives
• Additional notes on consistency issues (if any)

final ahpResult = AhpResult(
        results: ahpResultDetail..sort((a, b) => b.value.compareTo(a.value)), // list result
        isConsistentCriteria: consistencyCriteria.isConsistent, /// boolean
        consistencyCriteriaRatio: consistencyCriteria.ratio, // double
        isConsistentAlternative: alternativesConsistency[0].isConsistent, // boolean
        consistencyAlternativeRatio: alternativesConsistency[0].ratio, // double
        note: note, // string
      );

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🔍 Sample Case

1. Pairwise Comparison Matrix

	C1	C2	C3
C1	1.000	1.000	5.000
C2	1.000	1.000	7.000
C3	0.200	0.143	1.000

2. Matrix Normalization

Column totals:

• C1: 2.200
• C2: 2.143
• C3: 13.000

Normalized matrix:

	C1	C2	C3
C1	0.455	0.467	0.385
C2	0.455	0.467	0.538
C3	0.091	0.067	0.077

3. Eigenvector (Priority of Each Criterion)

Criterion	Priority
C1	0.436
C2	0.487
C3	0.078

Interpretation:

• C2 is the most important criterion (48.7%)
• Followed by C1 (43.6%)
• C3 has a low weight (7.8%)

4. Consistency Ratio (CR)

a. λ_max Value

λ_max = 3.078

b. Consistency Index (CI)

CI = (λ_max - n) / (n - 1) = (3.078 - 3) / 2 = 0.039

c. Consistency Ratio (CR)

Random Index (RI) for n=3 is 0.58

CR = CI / RI = 0.039 / 0.58 ≈ 0.067

5. Conclusion

• Consistency Ratio (CR) = 0.067 < 0.1, which means the level of consistency is still acceptable.
• The criterion weights can be used for further decision-making.

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⚙️ Architecture Notes

• 🧩 Uses immutable data classes (Criteria, Alternative, etc.)
• 🔑 Unique ID generation via internal _helper
• ⏱ Integrated performance profiling using Stopwatch
• 🧼 Strong validation with helpful exceptions:
  • Duplicate IDs
  • Empty inputs
  • Invalid matrix dimensions

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📈 Performance Profiling

Every major method logs:

• ⏱ Start and end timestamps.
• ⌛ Execution duration (in milliseconds).

Great for debugging and optimization during development.

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📌 Important

Because the calculations are performed on the client side, the total number of criteria and alternatives may impact your device’s performance. Please use the data wisely.

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🙋‍♂️ Contributing

We welcome all contributions and suggestions!

👉 Open an issue or submit a pull request at GitHub Repo