"AI-Powered Last-Mile Delivery Optimization"
A Routific-inspired route optimization dashboard that uses AI orchestration to coordinate spatial clustering and vehicle routing solvers for last-mile logistics.
Manual route planning can't keep up with 1,600 daily orders across two hubs, tight time windows, and complex capacity constraints.
Without automated constraint checking, drivers frequently arrive outside customer time windows, leading to failed deliveries and customer complaints.
Existing tools don't model cross-dock operations where inbound shipments are sorted and directly loaded onto outbound vehicles — forcing manual workarounds.
Route planners rely on gut feel and spreadsheets instead of AI-powered reasoning that considers time windows, capacity, and geographic clustering simultaneously.
Manages daily delivery schedules for a food distribution company in Montreal. Juggles 1,600 orders/day across multiple drivers and waves.
Handles real-time adjustments — reassigning orders when drivers are delayed or loads change. Needs to quickly visualize routes and resequence stops.
Oversees multiple depots and wants data-driven decisions on fleet sizing and route efficiency. Evaluates AI tools to reduce last-mile costs.
Manager uploads a CSV of 1,600 daily orders across two hubs with addresses, time windows, and quantities.
Set wave cutoffs, depot location, max pallets per route, batch sizes, and route count.
Geographic clustering groups nearby stops, then constraint-based routing solvers sequence and assign drivers.
Inspect routes on the map, review driver stats, drag-and-drop to resequence stops manually.
An LLM reasoning engine orchestrates spatial clustering and vehicle routing solvers — combining machine learning with operations research.
Leaflet-based map with color-coded routes, depot marker, stop popups, and real road geometry via OSRM routing.
Recharts analytics showing stops per driver, quantity load distribution, and wave breakdown with tabular summaries.
Manually resequence stops after optimization with grip handles, automatic recalculation, and distance updates.
Full control over wave cutoffs, batch sizes, pallet limits, route count, depot coordinates, and logging levels.
LLM orchestrates traditional operations research solvers, combining reasoning with mathematical optimization for superior results.
Driver stats and route maps update instantly after optimization, giving planners immediate visibility into load balance and coverage.
12 operational parameters let planners tune the system to their specific fleet, capacity, and scheduling constraints without code changes.