aerialblancaservices/drone-cvrp-neural-solver

Drone CVRP Neural Solver (Attention Model + POMO)

ONNX-exported neural network for solving Capacitated Vehicle Routing Problems (CVRP) in drone delivery contexts.

Architecture

• Encoder: 6-layer Transformer with 8-head multi-head attention (128-dim embeddings, 512-dim FFN)
• Decoder: Autoregressive pointer-network with glimpse mechanism (Kool et al. 2019)
• Input features: 4D per node — x, y, z (altitude), demand
• Training: REINFORCE with greedy rollout baseline, 1000 epochs on random CVRP-50 instances

The model supports dynamic batch and problem sizes up to 200 nodes.

ONNX Interface

Port	Name	Shape	Type
Input	locs	[batch, nodes, 3]	float32
Input	demand	[batch, nodes, 1]	float32
Input	capacity	[batch, 1]	float32
Output	actions	[batch, max_steps]	int64
Output	log_p	[batch, max_steps]	float32

The wrapper automatically prepends a depot at (0.5, 0.5, 0.0) with zero demand. Set capacity high (e.g. 999) for unconstrained TSP optimization, or use the trained value (40) for standard CVRP-50 benchmark routing.

Training Recipe

Parameter	Value
Problem size	50 customers
Batch size	512–1024
Epochs	500–1000
Learning rate	1e-4 (cosine annealing)
Warmup	10 epochs
Demand range	{1..9}
Vehicle capacity	40
Entropy bonus	0.01

Usage

import numpy as np
import onnxruntime as ort

session = ort.InferenceSession("cvrp50_model.onnx")

locs = np.random.rand(1, 10, 3).astype(np.float32)
demand = np.ones((1, 10, 1), dtype=np.float32)
capacity = np.array([[999.0]], dtype=np.float32)

actions, log_p = session.run(None, {
    "locs": locs,
    "demand": demand,
    "capacity": capacity,
})

print(actions[0, :20])

Demo

Try it live in the Drone VRP Space — side-by-side comparison with a nearest-neighbor heuristic, physics-based energy modeling, and interactive route visualization.

References

• Kool, van Hoof, Welling (2018). Attention, Learn to Solve Routing Problems! (arXiv:1803.08475)
• Kwon et al. (2020). POMO: Policy Optimization with Multiple Optima for Reinforcement Learning (arXiv:2010.16011)
• Dorling et al. (2017). Vehicle Routing Problems for Drone Delivery (arXiv:1609.02906)