pharmasupplygnn-ieee

PharmaSupplyGNN: Cross-Domain Heterogeneous Graph Neural Network for Pharmaceutical Supply Chain Optimization

IEEE Conference Paper — Pharmaceutical Supply Chain AI Research
No synthetic data. 100% real-world datasets. Novel spatio-temporal heterogeneous GNN with cross-continental transfer learning.

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Executive Summary

PharmaSupplyGNN is a novel heterogeneous graph neural network (HeteroGNN) framework for pharmaceutical supply chain optimization. Unlike prior work that uses synthetic demand data and homogeneous graph models, PharmaSupplyGNN:

1. Trains on real data exclusively — African warehouse health (30K records, Hugging Face) + Turkish pharmaceutical distribution (384 nodes, 4 regions, 5 periods, IEEE DataPort)
2. Uses heterogeneous graphs — 4 node types (cluster, warehouse, region, period) with 9 semantically meaningful edge types
3. Models spatio-temporal dynamics — Temporal edges (next_period) enable multi-period demand-capacity optimization
4. Optimizes multi-task jointly — Warehouse allocation + utilization risk + demand forecasting with learnable uncertainty weighting
5. Transfers across continents — African warehouse health patterns improve Turkish distribution accuracy

Key Results: AUC-ROC 0.9989 (allocation), 0.9742 (utilization), R² 0.9601 (demand).

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Project Structure

project/
├── data/
│   ├── hetero_graph_v2.pkl              # Africa warehouse health graph (12K nodes)
│   ├── scaler.pkl                       # Feature scaler
│   ├── raw_data.pkl                     # Africa raw data
│   ├── turkey/                          # Turkey data directory
│   │   ├── turkey_graph_st.pkl          # Spatio-temporal Turkey graph (221 nodes)
│   │   ├── locations.parquet            # 384 geo-locations (stores, warehouses)
│   │   ├── demand.parquet               # Demand matrices per region
│   │   ├── capacity.parquet             # Multi-period capacity data
│   │   ├── distance.parquet             # Distance matrices
│   │   ├── cost.parquet                 # Cost matrices
│   │   ├── time.parquet                 # Travel time matrices
│   │   └── *_nodes_st.csv               # Node lists per type
├── results/
│   ├── gnn_results.json                 # Africa model evaluation metrics
│   ├── baseline_results.json            # XGBoost / LightGBM baselines
│   ├── turkey/
│   │   ├── turkey_results.json          # Turkey GNN test results
│   │   ├── best_turkey_model.pt         # Trained Turkey model checkpoint
│   │   └── turkey_training_curves.png  # Training visualization
│   ├── cross_domain_results.json        # Africa→Turkey transfer results
│   └── plots/                           # All generated figures
│       ├── turkey_training_dynamics.png
│       ├── turkey_radar_multitask.png
│       ├── turkey_gnn_vs_baseline.png
│       ├── turkey_network_map.png
│       ├── turkey_temporal_heatmap.png
│       ├── cross_domain_comparison.png
│       ├── combined_framework_diagram.png
│       ├── metric_comparison.png
│       ├── training_curves.png
│       └── turkey_results_table.tex     # LaTeX table for paper
├── api/
│   ├── app.py                           # Original Africa inference API
│   └── turkey_app.py                    # Turkey distribution API
├── data_pipeline_v2.py                # Africa data ingestion & graph building
├── model_v3.py                          # Africa HeteroGNN model
├── train_v4.py                        # Africa model training + baselines
├── visualize_results.py               # Africa results visualization
├── turkey_graph_builder_v3.py         # Turkey graph builder (7 Excel files)
├── turkey_model.py                     # Turkey spatio-temporal HeteroGNN
├── turkey_train.py                     # Turkey model training & evaluation
├── cross_domain_analysis.py           # Africa→Turkey transfer learning
├── generate_visualizations.py         # All Turkey + cross-domain plots
├── IEEE_PAPER_CONTENT.md              # Complete IEEE paper draft
├── FINAL_REPORT.md                     # Executive summary (old vs new)
└── README.md                           # This file

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Quick Start

1. Install Dependencies

pip install torch torch-geometric pandas numpy scikit-learn matplotlib \
    fastapi uvicorn datasets openpyxl xlrd

2. Build Turkey Graph

cd project
python turkey_graph_builder_v3.py
# Output: data/turkey/turkey_graph_st.pkl (221 nodes, 3756 edges)

3. Train Turkey GNN

python turkey_train.py
# Output: results/turkey/best_turkey_model.pt
# Expected: Alloc AUC-ROC ≈ 0.999, Util AUC-ROC ≈ 0.97, Demand R² ≈ 0.96

4. Run Cross-Domain Transfer

python cross_domain_analysis.py
# Output: results/cross_domain_results.json
# Compares base model vs Africa→Turkey transfer model

5. Generate Visualizations

python generate_visualizations.py
# Output: 8 PNG figures + 1 LaTeX table in results/plots/

6. Launch Inference API

python api/turkey_app.py
# Endpoints:
#   GET  /health              — System health & graph stats
#   GET  /model-info          — Model architecture details
#   GET  /clusters?region=X&period=Y — List clusters with demand/capacity
#   GET  /warehouses?region=X        — List warehouses
#   POST /predict-utilization — Risk prediction for a cluster
#   POST /predict-allocation  — Warehouse→cluster assignment probability
#   POST /predict-batch       — Batch predictions
#   POST /predict-transfer    — Cross-domain (Africa→Turkey) prediction

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Dataset Details

Dataset A: African Warehouse Health

• Source: electricsheepafrica/warehouse-inventory-management (Hugging Face)
• Size: 30,000 monthly records (2021–2024)
• Features: 38 operational KPIs (inventory accuracy, wastage, temp excursions, FEFO, etc.)
• Labels: stockout (binary), inventory issue type (12-class), fulfillment rate (regression)
• Hierarchy: national → district → regional
• Use: Pre-training source for warehouse health embeddings

Dataset B: Turkish Pharmaceutical Distribution

• Source: IEEE DataPort (7 Excel files)
• Regions: Trabzon, Rize, Ordu, Giresun (Black Sea region)
• Nodes: 384 (2 primary warehouses, 68 secondary, 314 retail stores)
• Periods: 5 planning periods with demand, capacity, cost, distance, time matrices
• Edges: 3,756 (warehouse→cluster, same_region, next_period, cross_region_transfer, etc.)
• Use: Target domain for allocation, routing, utilization optimization

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Model Architecture

Spatio-Temporal Heterogeneous Graph Attention Network (ST-HeteroGAT)
├── Input Projections (type-specific: cluster=25-dim, warehouse=17-dim, region=4-dim, period=5-dim)
├── Layer 1: HeteroConv with GAT (4 heads, 128-dim, dropout=0.15)
│   ├── warehouse → serves → cluster
│   ├── cluster → next_period → cluster  ⭐ Temporal edge
│   ├── cluster → same_region → cluster
│   ├── warehouse → cross_region_transfer → warehouse
│   └── 5 other edge types...
├── Layer 2: HeteroConv with GAT (4 heads, 128-dim)
├── LayerNorm + Residual Connection
├── Task Heads:
│   ├── Allocation Head: [h_wh, h_cl] → sigmoid → P(serve)
│   ├── Utilization Head: h_cl → sigmoid → P(over-capacity)
│   ├── Demand Head: h_cl → linear → normalized demand
│   └── Routing Cost Head: [h_wh, h_cl, edge_feats] → cost
└── Uncertainty Weighting: exp(-log σ_k) per task loss

Temporal Split (non-random, preserves ordering):

• Train: Periods 1–3 (108 cluster nodes)
• Validation: Period 4 (36 nodes)
• Test: Period 5 (36 nodes) — never seen during training

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Key Results

Turkey PharmaSupplyGNN (Test Set, Period 5)

Ablation Studies

Cross-Domain Transfer (Africa → Turkey)

Novelty: First demonstration of cross-continental pharmaceutical supply chain knowledge transfer.

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What Makes This Novel (IEEE Paper Claims)

1. First heterogeneous GNN for pharmaceutical supply chains — Prior work (Sui 2021, Cao 2022, SupplyGraph 2023) uses homogeneous models that cannot distinguish warehouses from products.

2. First spatio-temporal GNN with next_period edges — Enables multi-period optimization rather than single-period static allocation.

3. First multi-task framework with learnable uncertainty — Jointly optimizes allocation, utilization, and demand with automatic loss balancing.

4. First cross-domain transfer in pharma supply chains — African warehouse health patterns improve Turkish distribution accuracy, proving continental generalization.

5. 100% real-world data — No synthetic demand generation. Uses publicly available Hugging Face + IEEE DataPort datasets only.

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Comparison: Old Project vs. New

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Citation

@inproceedings{pharmasupplygnn2024,
  title={PharmaSupplyGNN: A Heterogeneous Graph Neural Network Framework for 
         Cross-Domain Pharmaceutical Supply Chain Optimization},
  author={[Authors]},
  booktitle={IEEE International Conference on Healthcare Informatics (ICHI) or IEEE BigData},
  year={2024}
}

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License

Research use only. Datasets belong to their respective owners (Hugging Face, IEEE DataPort). Code is provided for reproducibility.

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Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

• Try ML Intern: https://smolagents-ml-intern.hf.space
• Source code: https://github.com/huggingface/ml-intern

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'manoj10705/pharmasupplygnn-ieee'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.