Waste Logistics Architecture

Technical Specification

Autonomous
Waste Logistics

A Neuro-Evolutionary Architecture resolving the Dynamic Vehicle Routing Problem with Stochastic Demands (DVRPSD) via Graph Neural Networks and State Space Models.

From Static to State-Dependent

Traditional logistics relies on deterministic schedules ("Every Tuesday"), leading to a bimodal failure: servicing empty bins (Air-Hauling) or ignoring full ones (Overflow).

The Cost of Determinism

40% of collections are premature (Zero Utility).
Sanitation risks from unpredicted overflow.

Efficiency Comparison

The Neural Stack

Integrating Geometric Deep Learning with Control Theory.

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1. Spatial Encoder (GNN)

Topology

Maps the city not as Euclidean coordinates, but as a topological graph. Learns "accessibility" via Message Passing.

$$ \mathbf{h}_i^{(l)} = \phi^{(l)} \left( \mathbf{h}_i^{(l-1)}, m_i^{(l)} \right) $$ $$ m_i^{(l)} = \text{AGGREGATE} \left( \{ \psi(\mathbf{h}_j^{(l-1)}, \mathbf{e}_{ij}) : j \in \mathcal{N}(i) \} \right) $$

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2. Temporal Dynamics (SSM)

Forecasting

Models waste generation as a continuous differential equation. Servicing a bin is a "Control Input" that resets the state.

$$ \mathbf{x}(t) = \mathbf{A}\mathbf{x}(t-1) + \mathbf{B}\mathbf{u}(t) $$ $$ y(t) = \mathbf{C}\mathbf{x}(t) + \mathbf{D}\mathbf{u}(t) $$

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3. Glimpse Attention

Filtering

Acts as an information bottleneck. Filters out irrelevant (empty) nodes to focus computational resources on hotspots.

$$ \mathbf{z}_{(i), j} = 10 \cdot \text{tanh} \left( \frac{Q_{(i)}^T K_j}{\sqrt{d_k}} \right) $$ $$ \text{p} (\mathbf{z})_{(i), j} = \frac{\exp{\mathbf{z}_{(i), j}}}{\sum_{k = 1}^K \exp{\mathbf{z}_{(i), k}}} $$

Input: Latent State

Compressed representation of only the active, critical bins.

Global: Genetic Algorithm

Maintains a population of routes. Uses "Ordered Crossover" to preserve good sequences.

Local: Beam Search

Refines solutions. Uses Neural Embeddings to calculate "distance" instantly.

Hybrid Optimization

Pure Deep Learning cannot guarantee hard constraints (capacity). Pure Heuristics are too slow.

The HGS-LBS engine combines the global search of Genetic Algorithms with the aggressive local refinement of Beam Search, guided by the neural embeddings.

Systemic Implications

Closed Loop Control

The "Route" is a control input. The system learns the impact of its own servicing actions on the city's state.

Scalability

The Glimpse module allows the system to scale to 10,000+ nodes by filtering noise before optimization begins.

Sustainability

Aligns operational spend with actual demand. Eliminates fuel waste from servicing empty bins.