| name | geo-infer-log |
| description | Logistics optimization including route planning, fleet management, delivery scheduling, and supply chain modeling. Use when optimizing delivery routes, managing fleets, analyzing supply chain resilience, or computing emissions from transportation. |
| prerequisites | {"required":["geo-infer-space","geo-infer-data"],"recommended":["geo-infer-time"]} |
| difficulty | intermediate |
| estimated_time | 45min |
| examples_dir | ../GEO-INFER-EXAMPLES/examples/ |
GEO-INFER-LOG
Instructions
Core Capabilities
- Delivery: KMeans clustering, Voronoi tessellation, Haversine service areas
- Transport: Dijkstra routing, betweenness centrality, max-flow, emissions
- Supply chain: PuLP MILP optimization, articulation points, EOQ, Monte Carlo
- Fleet management: Vehicle routing, real-time tracking, ETA calculation
- Observability: Enhanced structured logging with spatial context
Key Imports
from geo_infer_log import LastMileRouter, DeliveryScheduler, ServiceAreaAnalyzer
from geo_infer_log import EmissionsCalculator, TransportationNetworkAnalyzer
from geo_infer_log import SupplyChainModel, FacilityLocator, InventoryManager
from geo_infer_log import EnhancedLogger, PerformanceMetrics
All logistics classes are lazy-loaded via __getattr__ — zero cost until accessed.
Examples
from geo_infer_log import FacilityLocator
locator = FacilityLocator(n_facilities=5)
locations = locator.locate_facilities(demand_points)
coverage = locator.analyze_coverage(locations, demand_points)
Guidelines
- All implementations are real (KMeans, Dijkstra, PuLP) — no placeholders
- Submodules (
api, core, models, utils) lazy-loaded on attribute access
- Logger used for all output — no
print() statements
- Test:
uv run python -m pytest GEO-INFER-LOG/tests/ -v
Integrations
- ECON → Logistics cost feeding economic models
- TRANSPORT → Route optimization and emissions calculation
- RISK → Supply chain risk and disruption modeling
- SPACE → H3-based delivery zone tessellation
- OPS → Logistics operation monitoring
