wead / ahpd
A modern, data-driven implementation of the Analytic Hierarchy Process (AHP) algorithm for objective, multi-criteria decision-making, replacing subjective pairwise judgments with real-world quantitative inputs.
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Language:HTML
Type:php-ext
Ext name:ext-ahpd
pkg:composer/wead/ahpd
Requires
- php: >=8.1
This package is auto-updated.
Last update: 2025-10-27 12:36:34 UTC
README
AHPd is a 100% objective, multi-criteria decision-making system, representing a modern, data-driven evolution of the classic Analytic Hierarchy Process (AHP) method.
Unlike traditional AHP, AHPd eliminates subjective judgment. It uses real, measurable data to generate business decisions that are consistently auditable, mathematical, and justifiable.
🎯 Strategic Advantage: Bias-Free Decisions
In complex and highly regulated environments, subjectivity is the biggest risk. AHPd was built to eliminate this risk.
The AHPd system provides a quantitative framework for comparing alternatives—be it products, projects, suppliers, or strategies—based on their quantitative attributes (price, performance, quality, resource consumption, etc.).
| Key Benefit | Value for Decision Makers | 
|---|---|
| 100% Objective Decisions | Criteria weights are mathematically derived from real-world data, removing human bias and politics from complex choices. Risk Mitigation and guaranteed compliance. | 
| Full Explainability | Every result is detailed with the percentage contribution of each criterion and attribute. Allows for Investment Justification and instant result auditing. | 
| High Consistency & Reproducibility | No manual subjectivity. The same data always yields the same, fully justifiable, and unquestionable result. | 
| High Performance (C/Rust Core) | Implemented with a highly optimized C/Rust core to process large volumes of data quickly. Accelerates Time-to-Decision in real-time systems. | 
🧠 How AHPd Works: Simple Process, Auditable Result
AHPd automatically transforms raw data into strategic insights without the need for data pre-processing or normalization.
- Define Preferences (Minimum Input): Specify the criteria and indicate whether you want to maximize or minimize each one (e.g., "Maximize Quality," "Minimize Price").
- Provide Raw Data: Input the quantitative data for all options being compared. Data can be passed exactly as it exists in your system.
- AHPd Automatic Calculation:
- The system analyzes the statistical spread of values across all options.
- Automatically assigns the weight of importance to each criterion based on this spread.
- Determines the relative performance (priority) of each alternative.
 
- Final Ranking: The output is a clear, auditable percentage ranking, showing which alternative is best and why.
📊 Intuitive Example
Consider a device purchase decision. The user only needs to indicate whether a lower "price" is better or a larger "battery" is better.
| Option | Price US$ (Minimize) | Storage GB (Maximize) | Memory GB (Maximize) | Camera Mpx (Maximize) | Battery mAh (Maximize) | 
|---|---|---|---|---|---|
| Phone A | 9494 | 128 | 6 | 48 | 4323 | 
| Phone B | 4139 | 256 | 8 | 50 | 4500 | 
| Phone C | 4429 | 256 | 8 | 50 | 4300 | 
| Phone D | 1885 | 128 | 6 | 64 | 5065 | 
➡️ AHPd Ranking Result:
- Phone D — 33.6% (Winner)
- Phone B — 25.38%
- Phone C — 26.1%
- Phone A — 14.93%
The screenshot below intuitively shows the results, allowing you to see precisely how much each feature (criterion) contributed to the final ranking score. This visually validates the weights calculated by AHPd.
💡 Explanation: Phone D (33.6% final score) won because it was the lowest-priced alternative. AHPd calculated that the 'price US$' criterion had the greatest statistical disparity among all candidates, assigning it a massive weight of 46.0% in the decision model. This resulted in Price having a 33.3% contribution to Phone D's total score, outweighing the contribution of any other single criterion for all other alternatives.
Verbose results:
{
  "contribution": {
    "alternatives_contribution": {
      "by_criteria": {
        "Phone A": {
          "battery mAh": 25.27140468399798,
          "camera Mpx": 24.073235745226988,
          "memory GB": 22.7835981160184,
          "price US$": 10.151185142297878,
          "storage GB": 17.720576312458753
        },
        "Phone B": {
          "battery mAh": 18.725026772638852,
          "camera Mpx": 17.849621957062656,
          "memory GB": 21.623542027984474,
          "price US$": 16.57434354299879,
          "storage GB": 25.227465699315214
        },
        "Phone C": {
          "battery mAh": 18.24259984226682,
          "camera Mpx": 18.198574261252137,
          "memory GB": 22.046272819345443,
          "price US$": 15.791901454565918,
          "storage GB": 25.720651622569683
        },
        "Phone D": {
          "battery mAh": 19.309583049401244,
          "camera Mpx": 20.932567729107106,
          "memory GB": 14.85838512914299,
          "price US$": 33.34294232523744,
          "storage GB": 11.556521767111215
        }
      },
      "total_percentage": {
        "Phone A": 18.810524412740325,
        "Phone B": 26.42622428319317,
        "Phone C": 25.91950921187671,
        "Phone D": 28.843742092189796
      }
    },
    "criteria_weights": {
      "battery mAh": 0.05517554629857757,
      "camera Mpx": 0.09811746760930982,
      "memory GB": 0.11601983189243667,
      "price US$": 0.4599742131173236,
      "storage GB": 0.27071294108235233
    }
  },
  "rank": {
    "Phone A": 0.14922568130663832,
    "Phone B": 0.260912125126531,
    "Phone C": 0.25370960371891654,
    "Phone D": 0.336152589847914
  }
}
Detailed Analysis
AHPd assigned the following weights of importance to the criteria, highlighting the focus on Price:
| Criterion | AHPd Importance Weight | Interpretation | 
|---|---|---|
| price US$ (Minimize) | 45.99% | The largest variation among phones. This is the single decisive factor. | 
| storage GB (Maximize) | 27.07% | Second-largest data spread among the phones. | 
| memory GB (Maximize) | 11.60% | Relatively low variation among phones. | 
| camera Mpx (Maximize) | 9.81% | Medium variation among the data. | 
| battery mAh (Maximize) | 5.52% | The smallest data spread. This criterion barely influenced the decision. | 
Conclusion: AHPd objectively determined that the vast price difference among the candidates was the most relevant attribute for the final decision, given the distribution of the input data.
(The image visually demonstrates the percentage contribution of each criterion to the final ranking score, validating the weights calculated by AHPd.)
🧾 Practical Use Cases (Where AHPd Generates Value)
| Area | Typical Application | Strategic Outcome | 
|---|---|---|
| IT & Engineering | Selecting cloud architectures, choosing software/hardware vendors, prioritizing sprints. | Reduced Deployment Costs and increased system efficiency based on real performance data. | 
| Finance | Comparing investments based on return, risk, liquidity, and sustainability. | Automated Portfolio Optimization and risk alignment. | 
| Operations & HR | Choosing equipment, route optimization, or evaluating candidates/suppliers. | Measurable Consistency in selection processes and reduced operational overhead. | 
| Product & Marketing | Prioritizing features in roadmaps or comparative analysis of competitor products. | Data-Driven Roadmaps and clear competitive advantage. | 
🚀 Integration and Performance
AHPd is designed to be platform-agnostic and offer maximum performance, allowing the integration of real-time decision intelligence into your critical systems (BI, ERPs, recommendation systems).
| Type | Description | Link | 
|---|---|---|
| PHP Native Extension | Native C/Rust implementation for maximum performance within PHP systems. | 🔗 View PHP Documentation | 
| REST API | JSON-compatible web service for immediate integration with any programming language or BI tool. | 🔗 Online Service | 
| CLI Application | Command-line tool for direct use in automated pipelines and scripts. | 🔗 AHPd CLI | 
| GUI Application | Desktop application for end-user analysis and reporting. | (Planned) | 
📚 AHPd vs. Traditional AHP: The Data-Driven Evolution
This comparison highlights the key differences that make AHPd the ideal choice for automated and auditable systems, in contrast to the manual approach of classical AHP.
| Feature | AHPd (Data-Driven Evolution) | Traditional AHP (Classic Method) | 
|---|---|---|
| Input Source | Real, Quantitative Data (e.g., price, speed, capacity). | Subjective Judgments (Expert opinions, verbal comparisons). | 
| Criterion Weighting | Automatic. Mathematically derived from the data's statistical dispersion. | Manual. Derived from subjective pairwise comparisons of importance. | 
| Objectivity | Fully Objective. Consistent, unquestionable, and reproducible results. | Subjective/Semi-Objective. Depends on the consistency and bias of human judges. | 
| Primary Goal | Multi-criteria Optimization and Auditable Ranking based on performance. | Multi-criteria Prioritization based on perceived importance. | 
🧬 Licensing and Intellectual Property
The use and distribution of the AHPd system are free for both personal and commercial purposes. Compiled binaries, extensions, and libraries may be integrated into third-party products or services without additional licensing fees.
However, the high-performance computational core and underlying source code remain the exclusive intellectual property of Wead Technology®, ensuring integrity and continuous innovation.
Required Attribution
The use of AHPd requires mandatory attribution to Wead Technology® in your documentation, "About" section, or any licensing notices related to the product that integrates it.
Enterprise Services
Enterprise-grade services — including dedicated technical support, OEM integration, private cloud APIs, and performance optimization — are available for organizations seeking maximum scalability, reliability, and expert assistance.
For partnerships, large-scale deployments, or OEM licensing, contact Wead Technology® to discuss collaboration opportunities.
