🚚 𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰𝘀 𝗥𝗼𝘂𝘁𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 — 𝗯𝘂𝗶𝗹𝘁 (𝘀𝗮𝗻𝗲𝗹𝘆) 𝘄𝗶𝘁𝗵 𝗝𝗮𝘃𝗮

Same fleet. Same cities. Less miles. More on-time. Lower fuel.

That’s not magic — it’s constraints + streaming + smart heuristics.

Here’s the blueprint I’d ship today 👇

◾ Core problems you’ll solve

◾ VRP flavors: CVRP, VRPTW, multi-depot, pickup-&-delivery, cold-chain

◾ Hard constraints: capacity, time windows, driver shifts, service times

◾ Soft goals: distance, on-time %, CO₂, tolls, SLA penalties, fairness

◾ Java-first architecture

◾ Ingest → Kafka: orders, live GPS pings, traffic, depot status

◾ Store → Postgres (orders & depots), Redis (hot state), ClickHouse (historical KPIs)

◾ Routing graph → GraphHopper/OSRM (road speeds, truck profiles, turn costs)

◾ Optimizer → Java 21 service using OptaPlanner or OR-Tools (Java API)

◾ Stream brain → Flink/Kafka Streams for ETA updates & re-optimization triggers

◾ API/Gateway → Spring Boot / Quarkus (gRPC/REST) with SSE/WebSockets for live boards

◾ Obs → Micrometer + Prometheus + OpenTelemetry (per-route KPIs & solver stats)

◾ Optimization loop (fast & practical)

Build problem: vehicles, jobs, time windows, distances from routing graph

Seed solution: nearest-neighbor / sweep / savings heuristic

Publish: assign routes; stream ETAs to ops & drivers

React: when traffic or delays hit → incremental re-optimize affected region only

◾ Real-time realities (what actually works)

◾ Rolling horizon: lock near-term stops, optimize the tail — drivers don’t like teleporting routes

◾ Regional shards: city or depot partitions keep solve times predictable

◾ SLA safety: deadline guardrails override “shortest path” when needed

◾ What-if: sandbox a copy of live data for planners (no impact on drivers)

◾ Latency targets

◾ Small batch (≤200 stops/depot) initial plan: < 20–60s

◾ Incremental re-plan (spill, traffic, no-show): < 2–5s

◾ ETA refresh pipeline end-to-end: < 1s

Keep the UI streaming partial results — progress beats silence.

◾ Java tuning cheatsheet

◾ Java 21 virtual threads around IO (maps, DB, telemetry)

◾ G1/ZGC with small regions; avoid large object churn in scorers

◾ Precompute time-dependent travel times; avoid calling routing server inside inner loops

◾ “Planner overrides needed” — should trend down as the system learns

◾ Rollout in 4 sprints

Static nightly plans + live ETA dashboard

Mid-day incremental re-opt (traffic/incidents)

Driver app hooks (ack, proof-of-delivery) + SLA penalties in score

Multi-depot balancing, what-if simulator, cost/CO₂ optimization

Takeaway

Great logistics isn’t “solve once.”

It’s a Java loop that senses, decides, and adapts — all day, every day.