feat: add advanced manufacturing optimization framework

ADVANCED_FRAMEWORK_GUIDE.md

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+# 🎓 Advanced Manufacturing Optimization Framework
+
+Publication-ready experimental system for multi-objective job shop scheduling.
+
+## 📋 Table of Contents
+1. [Overview](#-overview)
+2. [Key Features](#-key-features)
+3. [Theoretical Foundation](#-theoretical-foundation)
+4. [Implemented Methods](#-implemented-methods)
+5. [Installation](#-installation)
+6. [Quick Start](#-quick-start)
+7. [Detailed Usage](#-detailed-usage)
+8. [Output Description](#-output-description)
+9. [Statistical Validation](#-statistical-validation)
+10. [Publication Guidelines](#-publication-guidelines)
+11. [Contributing](#-contributing)
+
+## 🎯 Overview
+The framework delivers a rigorous experimental platform for benchmarking optimization strategies in hybrid manufacturing. It is engineered to satisfy Q1 journal standards with stochastic simulation, statistical validation, and reproducible pipelines.
+
+**Target applications**
+- Hybrid manufacturing scheduling (job-shop, flow-shop, flexible cells)
+- Operations research experimentation with multi-objective objectives
+- Industry 4.0/5.0 digital twins and decision-support systems
+- Academic benchmarking of heuristics and metaheuristics
+
+## ⭐ Key Features
+| Dimension | Description |
+| --- | --- |
+| Scientific rigor | 30 replications, 95% CIs, Friedman + Wilcoxon tests, Cohen's *d*, reproducible seeds |
+| Algorithm portfolio | 12 methods (7 dispatching rules, 3 metaheuristics, 2 advanced multi-objective approaches) |
+| Simulation realism | Processing variability, energy uncertainty, machine breakdowns, learning effects |
+| Outputs | Publication-grade figures (300 DPI), LaTeX tables, CSV exports, markdown report |
+| Extensibility | Modular design for adding new methods, metrics, or scenarios |
+
+## 📚 Theoretical Foundation
+The framework optimizes a four-objective vector \((Z_1, Z_2, Z_3, Z_4)\) representing makespan, energy, material usage, and machine underutilization. Aggregation uses a weighted sum with configurable weights defaulting to \((0.35, 0.25, 0.20, 0.20)\).
+
+Stochastic simulation includes:
+- **Processing time variability**: \(T \sim \mathcal{N}(T_0, 0.1 T_0)\) with adaptive learning in the multi-objective scenario.
+- **Energy consumption**: Gamma-distributed deviations with tighter constraints under energy-constrained runs.
+- **Machine breakdowns**: Poisson probability per job with downtime samples from \(\mathcal{U}(10,30)\) minutes.
+- **Learning curves**: Power-law learning with exponent derived from a 5% improvement every doubling of jobs.
+- **Quality success**: Availability-dependent Bernoulli trials blending equipment reliability with schedule decisions.
+
+## 🧮 Implemented Methods
+### Classical Dispatching Rules
+- **FCFS** (First Come First Served)
+- **SPT** (Shortest Processing Time)
+- **LPT** (Longest Processing Time)
+- **EDD** (Earliest Due Date)
+- **Slack Time** (minimum slack priority)
+- **Critical Ratio**
+- **WSPT** (Weighted Shortest Processing Time)
+
+### Metaheuristics and Multi-objective Strategies
+- **Genetic Algorithm** (Dirichlet-weight evolution with elitism and mutation)
+- **Particle Swarm Optimization** (continuous weight exploration with inertia/cognitive/social terms)
+- **Simulated Annealing** (stochastic weight adaptation with exponential cooling)
+- **NSGA-II Approximation** (fast Pareto ranking on normalized objectives)
+- **Intelligent Multi-Agent Optimizer** (Pareto score + efficiency boosts + machine load balancing)
+
+## 💻 Installation
+```bash
+# optional virtual environment recommended
+pip install pandas numpy matplotlib seaborn scipy scikit-learn
+```
+The script auto-generates a synthetic dataset when `hybrid_manufacturing_categorical.csv` is absent.
+
+## 🚀 Quick Start
+```bash
+python advanced_manufacturing_optimization.py \
+    --methods FCFS SPT Intelligent_MultiAgent \
+    --replications 10 \
+    --scenarios baseline stochastic \
+    --max-jobs 120
+```
+Outputs are written to `advanced_optimization_results/` with plots, tables, LaTeX exports, and a comprehensive markdown report.
+
+## 🔧 Detailed Usage
+- `--methods`: optional list of method identifiers from the registry.
+- `--replications`: override the default 30 replications.
+- `--scenarios`: subset of scenarios (`baseline`, `stochastic`, `high_variability`, `energy_constrained`, `multi_objective`).
+- `--max-jobs`: truncate the dataset for exploratory runs.
+
+To register a custom method, extend `OptimizationMethods.registry()` with a callable returning a prioritized DataFrame.
+
+## 📊 Output Description
+- `tables/summary_statistics.csv`: aggregate metrics with 95% confidence intervals.
+- `tables/all_results.csv`: full replication-level data (50+ metrics).
+- `tables/effect_sizes.csv`: Cohen's *d* for every pairwise comparison.
+- `plots/*.png`: bar charts, box plots, radar charts, correlation heatmaps, Pareto fronts, status distributions, etc.
+- `latex/summary_table.tex`: publication-ready LaTeX table.
+- `statistics/*.json`: Friedman and Wilcoxon outcomes.
+- `EXPERIMENTAL_REPORT.md`: auto-generated executive summary.
+
+## 📈 Statistical Validation
+- **Global hypothesis**: Friedman test for each scenario.
+- **Pairwise**: Wilcoxon signed-rank with Bonferroni correction.
+- **Effect size**: Cohen's *d* classification (negligible/small/medium/large).
+- **Power**: ≥0.8 for medium effects with 30 replications.
+
+## 📝 Publication Guidelines
+Provide a detailed methodology, cite classical dispatching references (Conway et al. 1967; Jackson 1955; Baker & Trietsch 2013) and metaheuristic sources (Goldberg 1989; Kennedy & Eberhart 1995; Kirkpatrick et al. 1983; Deb et al. 2002). Include generated figures (300 DPI) and LaTeX tables directly in manuscripts (IEEE/ACM compatible).
+
+## 🤝 Contributing
+1. Fork the repository.
+2. Implement the enhancement with thorough docstrings.
+3. Add tests or validation scripts if feasible.
+4. Update this guide or the generated report if the methodology evolves.
+5. Submit a pull request describing experimental impacts.
+
+---
+*This documentation complements the automated report produced by the framework and captures the rationale behind the experimental design.*