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.dockerignore

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+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.pytest_cache/
+.cache/
+cache/
+.venv/
+venv/
+
+# Plots and binders generated during local exploration
+*.png
+*.pdf
+challenge_description.txt
+obsolete/
+
+# Local secrets — must never enter the image. Use env vars instead.
+seed/service-account.json
+.env
+.env.*
+
+# Editor / OS
+.vscode/
+.idea/
+.DS_Store
+Thumbs.db

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
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-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+cities_graph_dropoffs.png filter=lfs diff=lfs merge=lfs -text
+interhack26.pdf filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+FROM python:3.12-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    MPLBACKEND=Agg \
+    PORT=7860
+
+# Native deps for shapely / pyproj / osmnx.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        libgeos-dev \
+        libspatialindex-dev \
+        libproj-dev \
+        proj-bin \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# Install Python deps first so layer caches across code changes.
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy backend sources. .dockerignore filters out pycache, plots, secrets.
+COPY . .
+
+EXPOSE 7860
+
+# Listen on $PORT (set by HF Spaces, Cloud Run, Fly, Render). Fallback to 7860.
+CMD ["sh", "-c", "uvicorn app:app --host 0.0.0.0 --port ${PORT:-7860} --workers 1"]

README.md

@@ -1,10 +1,10 @@
----
-title: Interhack26
-emoji: 🚀
-colorFrom: blue
-colorTo: pink
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Interhack26
+emoji: 🚀
+colorFrom: blue
+colorTo: pink
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

analytics.py

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+"""KPIs, explanations, load profile, warehouse-staging recommendations.
+
+Everything here turns a `FleetPlan` from `solver.py` into the human-readable
+add-ons that make the demo pitch land:
+  - per-van capacity-over-time profile (returnables visualization)
+  - per-stop arrival reasons
+  - fleet-level KPIs vs the naive baseline (savings %, CO2, utilization)
+  - warehouse staging recommendations for warehouse prep
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from loader import Depot, Driver, Fleet, Stop
+from solver import FleetPlan, VanPlan
+from travel_time import TravelMatrix
+
+
+# UK DEFRA factor for diesel light commercial vehicle, kg CO2 per km.
+CO2_KG_PER_KM = 0.20
+
+
+@dataclass
+class LoadPoint:
+    after_stop: str
+    cells: int
+    kg: int
+
+
+@dataclass
+class KPIs:
+    fleet_drive_min: float
+    baseline_drive_min: float
+    savings_pct: float
+    fleet_km: float
+    baseline_km: float
+    co2_kg_saved: float
+    driver_utilization_pct: float
+    capacity_utilization_pct: float
+    stops_per_van: list[int]
+    feasible_vans: int
+    total_vans: int
+
+
+def _hm(s: int) -> str:
+    h, m = divmod(int(s) // 60, 60)
+    return f"{h:02d}:{m:02d}"
+
+
+def load_profile(van: VanPlan, stops_by_id: dict[str, Stop]) -> list[LoadPoint]:
+    """Truck departs depot fully loaded with all deliveries; load decreases as
+    we drop off and increases as we collect empties."""
+    if not van.stops:
+        return []
+    cells = sum(stops_by_id[p.id].delivery_cells for p in van.stops)
+    kg = sum(stops_by_id[p.id].delivery_kg for p in van.stops)
+    profile = [LoadPoint("DEPOT", int(cells), int(round(kg)))]
+    for p in van.stops:
+        s = stops_by_id[p.id]
+        cells = cells - s.delivery_cells + s.pickup_cells
+        kg = kg - s.delivery_kg + s.pickup_kg
+        profile.append(LoadPoint(p.id, int(cells), int(round(kg))))
+    return profile
+
+
+def explain_van(van: VanPlan, stops_by_id: dict[str, Stop], driver: Driver) -> list[str]:
+    """One line per visited stop, naming the driver-readable reason."""
+    out: list[str] = []
+    if not van.stops:
+        out.append(f"{driver.id} idle (no stops in this run).")
+        return out
+
+    first = van.stops[0]
+    s_first = stops_by_id[first.id]
+    idle_min = max(0, (first.arrival_s - driver.shift_start_s) // 60)
+    win_w_min = (s_first.t_close_s - s_first.t_open_s) // 60
+    if idle_min > 30:
+        out.append(
+            f"{driver.id} departs {_hm(driver.shift_start_s)}, parks at {first.id} "
+            f"by {_hm(first.arrival_s)} — its {win_w_min}min window opens at "
+            f"{_hm(s_first.t_open_s)}, so the truck waits then unloads at the open."
+        )
+    else:
+        out.append(
+            f"{driver.id} hits {first.id} at {_hm(first.arrival_s)} — "
+            f"first stop is the closest with an open window."
+        )
+
+    for prev_p, cur_p in zip(van.stops, van.stops[1:]):
+        s_cur = stops_by_id[cur_p.id]
+        slack_min = max(0, (s_cur.t_close_s - cur_p.arrival_s) // 60)
+        out.append(
+            f"  → {cur_p.id} at {_hm(cur_p.arrival_s)}, "
+            f"{slack_min}min before window {_hm(s_cur.t_close_s)} closes."
+        )
+
+    out.append(
+        f"  → returns to depot. Peak load {van.peak_cells} cells / "
+        f"{van.peak_kg} kg ({van.travel_s // 60} min driving)."
+    )
+    return out
+
+
+def _van_distance_km(van: VanPlan, matrix: TravelMatrix) -> float:
+    if not van.stops:
+        return 0.0
+    depot_i = matrix.index_of("DEPOT")
+    total_m = 0.0
+    prev = depot_i
+    for sp in van.stops:
+        i = matrix.index_of(sp.id)
+        total_m += float(matrix.dist_m[prev, i])
+        prev = i
+    total_m += float(matrix.dist_m[prev, depot_i])
+    return total_m / 1000.0
+
+
+def compute_kpis(
+    plan: FleetPlan,
+    baseline: FleetPlan,
+    fleet: Fleet,
+    drivers: list[Driver],
+    matrix: TravelMatrix,
+) -> KPIs:
+    drive_min = plan.drive_s / 60
+    base_drive_min = baseline.drive_s / 60
+    savings = (
+        ((base_drive_min - drive_min) / base_drive_min * 100)
+        if base_drive_min > 0 else 0.0
+    )
+
+    fleet_km = sum(_van_distance_km(v, matrix) for v in plan.vans)
+    base_km = sum(_van_distance_km(v, matrix) for v in baseline.vans)
+    co2_saved = max(0.0, (base_km - fleet_km) * CO2_KG_PER_KM)
+
+    busy = sum(v.total_s for v in plan.vans)
+    shift = sum(d.shift_end_s - d.shift_start_s for d in drivers)
+    driver_util = (busy / shift * 100) if shift > 0 else 0.0
+
+    used = [v for v in plan.vans if v.stops]
+    cap_util = (
+        sum(v.peak_cells / fleet.capacity_cells for v in used) / len(used) * 100
+        if used else 0.0
+    )
+
+    return KPIs(
+        fleet_drive_min=round(drive_min, 2),
+        baseline_drive_min=round(base_drive_min, 2),
+        savings_pct=round(savings, 1),
+        fleet_km=round(fleet_km, 2),
+        baseline_km=round(base_km, 2),
+        co2_kg_saved=round(co2_saved, 2),
+        driver_utilization_pct=round(driver_util, 1),
+        capacity_utilization_pct=round(cap_util, 1),
+        stops_per_van=[len(v.stops) for v in plan.vans],
+        feasible_vans=sum(1 for v in plan.vans if v.feasible),
+        total_vans=len(plan.vans),
+    )
+
+
+def warehouse_prep(
+    plan: FleetPlan,
+    request_stops: list[dict],
+    stops_by_id: dict[str, Stop],
+) -> list[str]:
+    """Per-van staging hints, derived from each van's actual delivery list."""
+    recs: list[str] = []
+    by_id = {s["id"]: s for s in request_stops}
+    for v in plan.vans:
+        if not v.stops:
+            continue
+        sku_qty: dict[str, int] = {}
+        total_returnable = 0
+        for sp in v.stops:
+            req = by_id[sp.id]
+            for line in req.get("deliveries", []):
+                sku_qty[line["product_id"]] = sku_qty.get(line["product_id"], 0) + line["qty"]
+            for line in req.get("pickups", []):
+                total_returnable += line["qty"]
+        top = sorted(sku_qty.items(), key=lambda x: -x[1])[:3]
+        recs.append(
+            f"{v.driver_id}: stage "
+            f"{', '.join(f'{q}× {p}' for p, q in top)} "
+            f"({sum(sku_qty.values())} units total)"
+        )
+        if total_returnable:
+            first = stops_by_id[v.stops[0].id]
+            recs.append(
+                f"  reserve ~{first.pickup_cells} returnable cells near {v.driver_id}'s door "
+                f"(pickups start at {v.stops[0].id})"
+            )
+    return recs

app.py

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+"""FastAPI server: OR-tools VRPSPD-TW + analytics layer.
+
+    GET  /health           liveness probe
+    GET  /sample-request   bundled example body
+    POST /optimize         main endpoint, returns plan + polylines + KPIs + explanations
+    POST /whatif           perturb the request (driver out, traffic, stop cancelled) and re-solve
+    POST /baseline         naive comparator alone
+    GET  /docs             auto Swagger UI
+"""
+
+from __future__ import annotations
+
+import json
+from contextlib import asynccontextmanager
+from pathlib import Path
+from typing import Literal
+
+import os
+import httpx
+from anthropic import Anthropic
+import numpy as np
+from fastapi import FastAPI, HTTPException, Response
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+
+from analytics import (
+    CO2_KG_PER_KM, KPIs, compute_kpis, explain_van, load_profile, warehouse_prep,
+)
+from baseline import solve_baseline
+from firestore_writer import build_route_doc, is_enabled as fs_enabled, write_routes
+from geometry import route_polyline
+from graph_manager import get_or_build_graph
+from loader import build_travel_matrix, load
+from solver import solve_vrp
+
+SAMPLE = Path(__file__).with_name("sample_request.json")
+PRODUCTS = Path(__file__).with_name("products.json")
+
+
+# --- Request schema (mirrors sample_request.json) ----------------------
+
+class Coords(BaseModel):
+    lat: float
+    lng: float
+
+
+class TimeWindow(BaseModel):
+    open: str
+    close: str
+
+
+class Line(BaseModel):
+    product_id: str
+    qty: int
+
+
+class StopReq(BaseModel):
+    id: str
+    coords: Coords
+    time_window: TimeWindow
+    deliveries: list[Line]
+    pickups: list[Line] = []
+
+
+class DepotReq(BaseModel):
+    id: str
+    coords: Coords
+    open: str
+    close: str
+
+
+class FleetReq(BaseModel):
+    num_vans: int
+    van_type: str
+    vans_ref: str | None = None
+    products_ref: str | None = None
+
+
+class DriverReq(BaseModel):
+    id: str
+    shift_start: str
+    shift_end: str
+
+
+class OptimizeRequest(BaseModel):
+    request_id: str | None = None
+    date: str | None = None
+    depot: DepotReq
+    fleet: FleetReq
+    drivers: list[DriverReq]
+    stops: list[StopReq]
+
+
+class Disruption(BaseModel):
+    type: Literal["driver_unavailable", "stop_cancelled", "traffic"]
+    driver_id: str | None = None
+    stop_id: str | None = None
+    multiplier: float | None = None     # for "traffic"
+
+
+class WhatIfRequest(BaseModel):
+    request: OptimizeRequest
+    disruption: Disruption
+
+class ChatRequest(BaseModel):
+    transcript: str
+    context: str
+
+class TTSRequest(BaseModel):
+    text: str
+
+
+# --- Response schema ---------------------------------------------------
+
+class StopOut(BaseModel):
+    sequence: int
+    id: str
+    arrival_time: str
+    coords: Coords
+
+
+class LoadPointOut(BaseModel):
+    after_stop: str
+    cells: int
+    kg: int
+
+
+class VanOut(BaseModel):
+    van_idx: int
+    driver_id: str
+    feasible: bool
+    travel_time_min: float
+    total_time_h: float
+    peak_cells: int
+    peak_kg: int
+    stops: list[StopOut]
+    polyline: list[Coords]
+    load_profile: list[LoadPointOut]
+    explanations: list[str]
+
+
+class KPIsOut(BaseModel):
+    fleet_drive_min: float
+    baseline_drive_min: float
+    savings_pct: float
+    fleet_km: float
+    baseline_km: float
+    co2_kg_saved: float
+    driver_utilization_pct: float
+    capacity_utilization_pct: float
+    stops_per_van: list[int]
+    feasible_vans: int
+    total_vans: int
+
+
+class OptimizeResponse(BaseModel):
+    request_id: str | None
+    fleet_drive_min: float
+    fleet_total_h: float
+    all_feasible: bool
+    depot: DepotReq
+    vans: list[VanOut]
+    kpis: KPIsOut
+    warehouse_prep: list[str]
+    firestore_written: int = 0
+
+
+# --- App ---------------------------------------------------------------
+
+@asynccontextmanager
+async def lifespan(_: FastAPI):
+    get_or_build_graph()
+    yield
+
+
+app = FastAPI(title="Damm Smart Truck API", version="2.0.0", lifespan=lifespan)
+app.add_middleware(
+    CORSMiddleware, allow_origins=["*"],
+    allow_methods=["GET", "POST"], allow_headers=["*"],
+)
+
+
+def _hm(s: int) -> str:
+    h, m = divmod(int(s) // 60, 60)
+    return f"{h:02d}:{m:02d}"
+
+
+def _run_pipeline(
+    body: dict,
+    *,
+    write_firestore: bool,
+    traffic_multiplier: float = 1.0,
+    time_limit_s: int = 5,
+) -> OptimizeResponse:
+    """Shared core for /optimize and /whatif."""
+    try:
+        depot, fleet, drivers, stops = load(body)
+    except KeyError as e:
+        raise HTTPException(422, f"unknown reference: {e}")
+    if not drivers:
+        raise HTTPException(422, "no drivers in fleet")
+    if len(drivers) != fleet.num_vans:
+        raise HTTPException(
+            422, f"num_vans={fleet.num_vans} but {len(drivers)} drivers"
+        )
+    if not stops:
+        raise HTTPException(422, "no stops to deliver")
+
+    matrix = build_travel_matrix(depot, stops)
+    if traffic_multiplier != 1.0:
+        matrix.time_s = (matrix.time_s * float(traffic_multiplier)).astype(np.float32)
+
+    plan = solve_vrp(depot, fleet, drivers, stops, matrix, time_limit_s=time_limit_s)
+    if not plan.vans:
+        # The disruption made the day infeasible — return a structured
+        # "no plan" response instead of 500 so the UI can show it.
+        return OptimizeResponse(
+            request_id=body.get("request_id"),
+            fleet_drive_min=0.0,
+            fleet_total_h=0.0,
+            all_feasible=False,
+            depot=DepotReq(**body["depot"]),
+            vans=[],
+            kpis=KPIsOut(
+                fleet_drive_min=0.0, baseline_drive_min=0.0, savings_pct=0.0,
+                fleet_km=0.0, baseline_km=0.0, co2_kg_saved=0.0,
+                driver_utilization_pct=0.0, capacity_utilization_pct=0.0,
+                stops_per_van=[], feasible_vans=0, total_vans=fleet.num_vans,
+            ),
+            warehouse_prep=[
+                "INFEASIBLE: no valid plan exists for this scenario. "
+                "Try relaxing time windows, adding a backup driver, or rescheduling stops."
+            ],
+            firestore_written=0,
+        )
+
+    baseline_plan = solve_baseline(depot, fleet, drivers, stops, matrix)
+
+    stops_by_id = {s.id: s for s in stops}
+    graph = get_or_build_graph()
+
+    vans_out: list[VanOut] = []
+    for v in plan.vans:
+        sequence = ["DEPOT"] + [p.id for p in v.stops] + ["DEPOT"]
+        polyline = (
+            [Coords(lat=lat, lng=lng) for lat, lng in route_polyline(graph, matrix, sequence)]
+            if v.stops else []
+        )
+        prof = load_profile(v, stops_by_id)
+        vans_out.append(VanOut(
+            van_idx=v.van_idx,
+            driver_id=v.driver_id,
+            feasible=v.feasible,
+            travel_time_min=round(v.travel_s / 60, 2),
+            total_time_h=round(v.total_s / 3600, 3),
+            peak_cells=v.peak_cells,
+            peak_kg=v.peak_kg,
+            stops=[
+                StopOut(
+                    sequence=p.sequence, id=p.id,
+                    arrival_time=_hm(p.arrival_s),
+                    coords=Coords(lat=stops_by_id[p.id].lat, lng=stops_by_id[p.id].lng),
+                )
+                for p in v.stops
+            ],
+            polyline=polyline,
+            load_profile=[
+                LoadPointOut(after_stop=lp.after_stop, cells=lp.cells, kg=lp.kg)
+                for lp in prof
+            ],
+            explanations=explain_van(v, stops_by_id, drivers[v.van_idx]),
+        ))
+
+    kpis = compute_kpis(plan, baseline_plan, fleet, drivers, matrix)
+    prep = warehouse_prep(plan, body["stops"], stops_by_id)
+
+    written = 0
+    if write_firestore and fs_enabled():
+        service_times_s = {s.id: s.service_time_s for s in stops}
+        docs = [
+            build_route_doc(
+                driver_id=v.driver_id,
+                truck_id=f"T-{v.van_idx + 1:02d}",
+                van_type=body["fleet"]["van_type"],
+                depot=body["depot"],
+                request_stops=body["stops"],
+                van_plan=v,
+                service_times_s=service_times_s,
+            )
+            for v in plan.vans
+        ]
+        written = write_routes(docs)
+
+    return OptimizeResponse(
+        request_id=body.get("request_id"),
+        fleet_drive_min=round(plan.drive_s / 60, 2),
+        fleet_total_h=round(plan.total_s / 3600, 3),
+        all_feasible=plan.all_feasible,
+        depot=DepotReq(**body["depot"]),
+        vans=vans_out,
+        kpis=KPIsOut(**kpis.__dict__),
+        warehouse_prep=prep,
+        firestore_written=written,
+    )
+
+
+# --- Endpoints ---------------------------------------------------------
+
+@app.get("/health")
+def health():
+    return {"status": "ok"}
+
+
+@app.get("/sample-request", response_model=OptimizeRequest)
+def sample_request():
+    if not SAMPLE.exists():
+        raise HTTPException(404, "sample_request.json missing")
+    return json.loads(SAMPLE.read_text(encoding="utf-8"))
+
+
+@app.get("/products")
+def products():
+    if not PRODUCTS.exists():
+        raise HTTPException(404, "products.json missing")
+    return json.loads(PRODUCTS.read_text(encoding="utf-8"))
+
+
+@app.post("/optimize", response_model=OptimizeResponse)
+def optimize(req: OptimizeRequest) -> OptimizeResponse:
+    return _run_pipeline(req.model_dump(), write_firestore=True)
+
+
+@app.post("/whatif", response_model=OptimizeResponse)
+def whatif(req: WhatIfRequest) -> OptimizeResponse:
+    body = req.request.model_dump()
+    multiplier = 1.0
+    d = req.disruption
+
+    if d.type == "driver_unavailable":
+        if not d.driver_id:
+            raise HTTPException(422, "driver_id required for driver_unavailable")
+        body["drivers"] = [x for x in body["drivers"] if x["id"] != d.driver_id]
+        body["fleet"]["num_vans"] = len(body["drivers"])
+    elif d.type == "stop_cancelled":
+        if not d.stop_id:
+            raise HTTPException(422, "stop_id required for stop_cancelled")
+        body["stops"] = [s for s in body["stops"] if s["id"] != d.stop_id]
+    elif d.type == "traffic":
+        multiplier = float(d.multiplier or 1.3)
+
+    # What-ifs are exploratory; don't overwrite the real plan in Firestore.
+    # Larger time budget — disrupted scenarios are tighter to solve.
+    return _run_pipeline(
+        body, write_firestore=False, traffic_multiplier=multiplier, time_limit_s=15,
+    )
+
+
+@app.post("/baseline", response_model=OptimizeResponse)
+def baseline_endpoint(req: OptimizeRequest) -> OptimizeResponse:
+    """Run only the naive baseline (for explicit comparison demos)."""
+    body = req.model_dump()
+    depot, fleet, drivers, stops = load(body)
+    matrix = build_travel_matrix(depot, stops)
+    plan = solve_baseline(depot, fleet, drivers, stops, matrix)
+    stops_by_id = {s.id: s for s in stops}
+    graph = get_or_build_graph()
+
+    vans_out: list[VanOut] = []
+    for v in plan.vans:
+        sequence = ["DEPOT"] + [p.id for p in v.stops] + ["DEPOT"]
+        polyline = (
+            [Coords(lat=lat, lng=lng) for lat, lng in route_polyline(graph, matrix, sequence)]
+            if v.stops else []
+        )
+        vans_out.append(VanOut(
+            van_idx=v.van_idx, driver_id=v.driver_id, feasible=v.feasible,
+            travel_time_min=round(v.travel_s / 60, 2),
+            total_time_h=round(v.total_s / 3600, 3),
+            peak_cells=v.peak_cells, peak_kg=v.peak_kg,
+            stops=[
+                StopOut(
+                    sequence=p.sequence, id=p.id,
+                    arrival_time=_hm(p.arrival_s),
+                    coords=Coords(lat=stops_by_id[p.id].lat, lng=stops_by_id[p.id].lng),
+                )
+                for p in v.stops
+            ],
+            polyline=polyline,
+            load_profile=[
+                LoadPointOut(after_stop=lp.after_stop, cells=lp.cells, kg=lp.kg)
+                for lp in load_profile(v, stops_by_id)
+            ],
+            explanations=explain_van(v, stops_by_id, drivers[v.van_idx]),
+        ))
+
+    return OptimizeResponse(
+        request_id=req.request_id,
+        fleet_drive_min=round(plan.drive_s / 60, 2),
+        fleet_total_h=round(plan.total_s / 3600, 3),
+        all_feasible=plan.all_feasible,
+        depot=req.depot,
+        vans=vans_out,
+        kpis=KPIsOut(
+            fleet_drive_min=round(plan.drive_s / 60, 2),
+            baseline_drive_min=round(plan.drive_s / 60, 2),
+            savings_pct=0.0,
+            fleet_km=0.0, baseline_km=0.0, co2_kg_saved=0.0,
+            driver_utilization_pct=0.0, capacity_utilization_pct=0.0,
+            stops_per_van=[len(v.stops) for v in plan.vans],
+            feasible_vans=sum(1 for v in plan.vans if v.feasible),
+            total_vans=len(plan.vans),
+        ),
+        warehouse_prep=warehouse_prep(plan, body["stops"], stops_by_id),
+        firestore_written=0,
+    )
+
+@app.post("/api/chat")
+def chat_endpoint(req: ChatRequest):
+    api_key = os.environ.get("ANTHROPIC_API_KEY", "")
+    if not api_key:
+        raise HTTPException(500, "ANTHROPIC_API_KEY not configured")
+    client = Anthropic(api_key=api_key)
+    system = (
+        "You are a hands-free voice assistant for Damm Motion delivery drivers.\n\n"
+        "Current route status:\n" + req.context + "\n\n"
+        "Based on what the driver said, decide the best action and give a short spoken reply (max 2 sentences).\n"
+        "Respond in the same language the driver used. Be concise — this will be read aloud.\n"
+        "You MUST return ONLY valid JSON matching this schema:\n"
+        "{\n"
+        '  "action": "mark_delivered" | "next_stop" | "navigate" | "status" | "unknown",\n'
+        '  "response": "..."\n'
+        "}"
+    )
+    try:
+        response = client.messages.create(
+            model="claude-3-haiku-20240307",
+            max_tokens=200,
+            system=system,
+            messages=[{"role": "user", "content": req.transcript}]
+        )
+        return json.loads(response.content[0].text)
+    except Exception as e:
+        raise HTTPException(500, f"Anthropic API error: {e}")
+
+@app.post("/api/tts")
+def tts_endpoint(req: TTSRequest):
+    api_key = os.environ.get("ELEVENLABS_API_KEY", "")
+    voice_id = os.environ.get("ELEVENLABS_VOICE_ID", "21m00Tcm4TlvDq8ikWAM")
+    if not api_key:
+        raise HTTPException(500, "ELEVENLABS_API_KEY not configured")
+    url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}"
+    headers = {
+        "xi-api-key": api_key,
+        "Content-Type": "application/json"
+    }
+    payload = {
+        "text": req.text,
+        "model_id": "eleven_multilingual_v2",
+        "voice_settings": {"stability": 0.5, "similarity_boost": 0.75}
+    }
+    try:
+        with httpx.Client() as client:
+            resp = client.post(url, headers=headers, json=payload, timeout=10.0)
+            resp.raise_for_status()
+            return Response(content=resp.content, media_type="audio/mpeg")
+    except Exception as e:
+        raise HTTPException(500, f"ElevenLabs API error: {e}")
+
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=False)

apt.txt

@@ -0,0 +1,4 @@
+libgeos-dev
+libspatialindex-dev
+libproj-dev
+proj-bin

baseline.py

@@ -0,0 +1,93 @@
+"""Naive comparator: polar sweep + per-van nearest-neighbor.
+
+Sort stops by angle from depot, round-robin assign to vans, then NN-order
+each van's stops from the depot. No window or capacity guarantee — that's
+the point. The OR-tools solver's "X% better" pitch number is computed
+against this baseline.
+"""
+
+from __future__ import annotations
+
+import math
+
+from loader import Depot, Driver, Fleet, Stop
+from solver import FleetPlan, StopPlan, VanPlan
+from travel_time import TravelMatrix
+
+
+def _polar(s: Stop, depot: Depot) -> float:
+    return math.atan2(s.lat - depot.lat, s.lng - depot.lng)
+
+
+def solve_baseline(
+    depot: Depot,
+    fleet: Fleet,
+    drivers: list[Driver],
+    stops: list[Stop],
+    matrix: TravelMatrix,
+) -> FleetPlan:
+    if not stops:
+        return FleetPlan([], 0, 0, True)
+
+    K = fleet.num_vans
+    swept = sorted(stops, key=lambda s: _polar(s, depot))
+    assigned: list[list[Stop]] = [[] for _ in range(K)]
+    for i, s in enumerate(swept):
+        assigned[i % K].append(s)
+
+    vans: list[VanPlan] = []
+    fleet_drive = fleet_total = 0
+    all_feas = True
+    depot_i = matrix.index_of("DEPOT")
+
+    for vid, van_stops in enumerate(assigned):
+        order: list[Stop] = []
+        remaining = {s.id: s for s in van_stops}
+        cur = depot_i
+        while remaining:
+            nxt = min(
+                remaining.values(),
+                key=lambda s: float(matrix.time_s[cur, matrix.index_of(s.id)]),
+            )
+            order.append(nxt)
+            cur = matrix.index_of(nxt.id)
+            del remaining[nxt.id]
+
+        # Simulate
+        t = float(drivers[vid].shift_start_s)
+        prev = depot_i
+        plan: list[StopPlan] = []
+        v_drive = cum_c = cum_k = peak_c = peak_k = 0
+        late = False
+        for seq, s in enumerate(order, start=1):
+            i = matrix.index_of(s.id)
+            travel = float(matrix.time_s[prev, i])
+            v_drive += int(travel)
+            t += travel
+            if t < s.t_open_s:
+                t = float(s.t_open_s)
+            if t > s.t_close_s:
+                late = True
+            plan.append(StopPlan(seq, s.id, int(t)))
+            t += s.service_time_s
+            cum_c += s.delivery_cells + s.pickup_cells
+            cum_k += int(round(s.delivery_kg + s.pickup_kg))
+            peak_c = max(peak_c, cum_c)
+            peak_k = max(peak_k, cum_k)
+            prev = i
+        ret = float(matrix.time_s[prev, depot_i])
+        v_drive += int(ret)
+        t += ret
+        v_total = int(t - drivers[vid].shift_start_s)
+        shift_ok = t <= drivers[vid].shift_end_s
+        cap_ok = peak_c <= fleet.capacity_cells and peak_k <= fleet.capacity_kg
+        feas = shift_ok and cap_ok and not late
+        all_feas &= feas
+        vans.append(VanPlan(
+            van_idx=vid, driver_id=drivers[vid].id, stops=plan,
+            travel_s=v_drive, total_s=v_total,
+            peak_cells=peak_c, peak_kg=peak_k, feasible=feas,
+        ))
+        fleet_drive += v_drive
+        fleet_total += v_total
+    return FleetPlan(vans, fleet_drive, fleet_total, all_feas)

cache/090c41e37bdb93f0bcebd4b4bc1f40b7bab09935.json

@@ -0,0 +1 @@
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cache/8c72638f670e14db619bc7fb41926d3eda0b3a8c.json

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challenge_description.txt

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+Damm Smart Truck
+Damm · Interhack BCN 2026
+1. Context i motivació
+Damm és una companyia amb una activitat logística d'alta complexitat, vinculada a la distribució de productes a múltiples canals i punts de venda. En aquest context, DDI (Distribució Directa Integral) té un paper clau com a xarxa de distribució del grup, amb un catàleg multiproducte de marques de primer nivell i presència a la Península Ibèrica, les Illes Balears i les Illes Canàries. DDI ofereix un servei àgil, flexible i personalitzat als seus clients, especialment en el canal hostaleria. Cada dia, els equips de distribució han de preparar, carregar i repartir comandes a diferents clients, amb rutes que poden incloure aproximadament entre 15 i 25 entregues per camió.
+Actualment, la preparació de la càrrega es fa principalment agrupant els productes per referència. Aquest model facilita la preparació al magatzem i permet aprofitar millor l'espai disponible al camió. Tot i així, durant el repartiment, el xofer pot haver de buscar productes en diferents punts del vehicle per completar l'entrega de cada client, cosa que pot generar moviments addicionals, pèrdues de temps i ineficiències en la descàrrega.
+A més, l'ordre de repartiment no està completament predefinit: sovint és el mateix xofer qui acaba decidint la ruta segons el seu coneixement del territori, els clients i les condicions del dia. Això obre una oportunitat per explorar com la tecnologia, les dades i l'optimització poden ajudar a prendre millors decisions combinant dues dimensions que habitualment es tracten per separat: la ruta de repartiment i la preparació física de la càrrega del camió.
+El repte també ha de tenir en compte la realitat operativa de DDI: diferents tipologies i volums de producte, capacitat limitada del camió, restriccions de manipulació, accés lateral als palets mitjançant lones, horaris o preferències dels clients, i logística inversa. Aproximadament un 60% dels productes entregats són retornables, de manera que els camions també recullen caixes, envasos o barrils buits durant la ruta.
+Aquest repte és rellevant perquè una millor coordinació entre ruta, càrrega i operativa pot generar impacte en eficiència, temps de repartiment, ergonomia, sostenibilitat, qualitat del servei i escalabilitat del model logístic.
+
+2. Objectiu del repte
+L'objectiu del repte és dissenyar una solució que permeti optimitzar de manera conjunta la ruta de repartiment i la configuració de la càrrega del camió, tenint en compte l'ordre d'entrega, el volum i tipologia dels productes, la capacitat del vehicle, la logística inversa i les restriccions operatives del procés actual.
+La solució ideal hauria de ser capaç de proposar una ruta eficient i, alhora, recomanar com preparar físicament la càrrega perquè la descàrrega sigui més àgil i coherent amb l'ordre de repartiment, sense penalitzar excessivament l'aprofitament de l'espai ni la preparació al magatzem.
+Es valoraran especialment les solucions que combinin un enfocament tècnic sòlid amb una aplicació realista al context logístic de Damm. No es tracta només de trobar la ruta més curta, sinó de trobar el millor equilibri entre eficiència de ruta, eficiència de càrrega, facilitat de descàrrega i viabilitat operativa.
+
+3. Repte tècnic / requisits funcionals
+La solució proposada hauria de cobrir, com a mínim, els següents elements:
+* Optimització de ruta: Proposar un ordre de visita dels clients tenint en compte variables com distància, temps estimat de trajecte, número de clients, possibles franges horàries, restriccions d'entrega o criteris de prioritat. 
+* Optimització de càrrega: Recomanar una configuració física de la càrrega del camió tenint en compte els productes de cada client, el volum, la tipologia de producte, la capacitat del vehicle i l'ordre previst de descàrrega. 
+* Equilibri entre càrrega per referència i càrrega per client: Explorar si convé mantenir una càrrega agrupada per referències, passar a una càrrega agrupada per client o proposar un model híbrid que maximitzi l'eficiència global. 
+* Visualització de la càrrega: Incloure una representació visual, encara que sigui simplificada, de com quedaria carregat el camió: zones, palets, grups de producte, clients associats o ordre recomanat de descàrrega. 
+* Logística inversa: Incorporar la recollida de retornables com una variable del model. La solució hauria de considerar que el camió no només entrega productes, sinó que també pot anar recuperant caixes, envasos o barrils buits durant la ruta. 
+* Restriccions operatives reals: Tenir en compte que els camions disposen de lones laterals que permeten accedir als palets des del lateral, i que la càrrega ha de ser viable des del punt de vista de magatzem, manipulació, seguretat i temps de preparació. 
+* Layout del magatzem: Considerar, com a variable opcional o extensió del repte, si una disposició diferent del magatzem podria facilitar la preparació de càrregues més eficients segons ruta, client o tipologia de producte. 
+* Recomanacions automàtiques: Generar recomanacions accionables per als equips de logística, com ara: millor ordre de càrrega, clients que convé agrupar, productes crítics, punts de fricció esperats, o alertes sobre configuracions poc eficients. 
+* Explicabilitat de la solució: La proposta hauria d'explicar per què recomana una ruta o configuració determinada, quins criteris ha prioritzat i quins compromisos assumeix entre espai, temps, facilitat de descàrrega i eficiència global. 
+La solució pot adoptar diferents formes: algoritme d'optimització, prototip funcional, simulador, eina visual, dashboard interactiu, model de recomanació o combinació d'aquests elements.
+
+4. Recursos i dades disponibles
+Damm facilitarà als equips informació de context i, en la mesura del possible, dades de mostra o dades sintètiques per treballar el repte. Els recursos previstos són:
+* Dades de mostra de comandes i repartiment de DDI: Exemples de comandes amb clients, referències de producte, quantitats, formats, volums o unitats logístiques aproximades. 
+* Exemples de rutes o assignació de clients: Mostres de com poden quedar assignats diferents clients a un xofer o camió en una jornada de repartiment. 
+* Informació sobre el funcionament actual de DDI: Explicació del procés actual de preparació de càrrega, criteris utilitzats, limitacions i punts de fricció identificats. 
+* Imatges o esquemes del magatzem: Material visual que ajudi a entendre com s'organitzen actualment les referències i com es prepara la càrrega. 
+* Imatges o esquemes de càrrega de camions: Exemples de com es distribueixen actualment els productes dins dels camions. 
+* Restriccions operatives simplificades: Capacitat estimada del camió, accés lateral, tipus de productes, manipulació, limitacions de volum o pes, i consideracions bàsiques de seguretat. 
+* Informació sobre logística inversa: Estimació del percentatge de productes retornables i exemples de retorns típics durant una ruta. 
+* Mentors o experts de Damm i DDI: Durant l'esdeveniment, Damm i DDI intentaran comptar amb persones de l'equip de Distribució i de Data & IA per resoldre dubtes operatius i tècnics dels participants.
+En cas que no es puguin compartir dades reals per motius de confidencialitat, es facilitaran dades anonimitzades, simplificades o sintètiques que reprodueixin la lògica del problema.
+
+5. Entregables esperats
+Al final del repte, cada equip haurà de presentar una proposta que permeti entendre tant la solució tècnica com la seva aplicació pràctica al context de Damm.
+Els entregables esperats són:
+* Prototip funcional o demo conceptual: Una eina, model, simulador, script, dashboard o interfície que mostri com es podria optimitzar ruta i càrrega de manera conjunta. 
+* Algoritme o lògica de decisió: Explicació clara de l'enfocament utilitzat: criteris d'optimització, variables considerades, restriccions incorporades i prioritats del model. 
+* Visualització de la solució: Representació visual de la ruta proposada i de la configuració recomanada de la càrrega del camió. 
+* Aplicació a un cas d'ús concret: Exemple pràctic amb una ruta simulada o de mostra, indicant quins clients es visiten, en quin ordre, com es carrega el camió i quins beneficis s'esperen. 
+* Resultats i impacte esperat: Estimació qualitativa o quantitativa del possible impacte: reducció de temps de descarga, millor aprofitament del camió, menys moviments, menys quilòmetres, millor experiència del xofer o més eficiència operativa. 
+* Pitch final: Presentació breu amb el problema abordat, la solució proposada, els supòsits utilitzats, les limitacions, els resultats i els següents passos per portar-ho a un pilot real. 
+No és imprescindible que la solució estigui completament acabada, però sí que ha de ser comprensible, defensable i aplicable a un escenari logístic realista.
+
+6. Criteris d'avaluació
+Les propostes s'avaluaran segons els criteris següents:
+* Aplicabilitat real al context de Damm (30%): La solució té en compte les restriccions operatives reals? És viable en un entorn de distribució com el de Damm? Entén bé el rol del magatzem, el xofer, la càrrega i la descàrrega? 
+* Qualitat tècnica de la solució (25%): L'enfocament d'optimització és sòlid? Les variables estan ben definides? La solució combina correctament ruta, càrrega i restriccions? El prototip o model és coherent? 
+* Impacte potencial (20%): La proposta pot generar millores rellevants en eficiència, temps, sostenibilitat, qualitat del servei, ergonomia o escalabilitat? 
+* Creativitat i originalitat (15%): L'equip proposa un enfocament diferencial? Explora solucions híbrides, visualitzacions útils, recomanacions intel·ligents o noves formes de prendre decisions? 
+* Claredat de comunicació i pitch (10%): La solució s'explica de manera clara? Els supòsits, resultats i limitacions són comprensibles? El pitch ajuda a imaginar com es podria portar la proposta a la pràctica? 
+Es valoraran especialment les propostes que no només optimitzin sobre el paper, sinó que demostrin sensibilitat pel funcionament real de l'operativa logística: preparació al magatzem, càrrega del camió, accés als productes, decisió del xofer, retornables i experiència de descàrrega al client.

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+9100643360,Carrer d'Anselm Clav� 70,8186,LLI�� D'AMUNT
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+9100669500,Carrer de Mary Santpere 1,8150,PARETS DEL VALLES
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+9100577693,"AVENIDA BALNEARI, S/N S/N",8551,TONA
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+119751,AVENIDA RABASSAIRES 40,8100,MOLLET DEL VALL�S
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+9100745478,Avinguda d'Ol�mpia 7,8500,VIC
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+9100296409,Carrer del Gran Vial 3,8170,MONTORN�S DEL VALL�S
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+9100636763,Carrer de Sant Isidre 13,8170,Montorn�s del Vall�s
+9100669904,Carrer de Carles Riba 3,8170,MONTORN�S DEL VALL�S
+9100702913,Carrer del Sol 3,8170,MONTORN�S DEL VALL�S
+9100724298,Carrer de Jacint Verdaguer 9,8170,MONTORN�S DEL VALL�S
+9100725292,Carrer la Pau 10,8170,MONTORN�S DEL VALL�S
+9100726583,Carrer Major 57,8170,MONTORN�S DEL VALL�S
+9100728401,Carrer de Sant Isidre 16,8170,MONTORN�S DEL VALL�S
+9100728505,Pla�a del Poble 14,8170,MONTORN�S DEL VALL�S
+9100729533,BV-5001 32-34,8170,MONTORN�S DEL VALL�S
+9100752742,Carrer de Federico Garc�a Lorca 2,8170,MONTORN�S DEL VALL�S
+9100752760,CALLE CONSELL DE CENT 5,8170,MONTORN�S DEL VALL�S
+9100760817,Carrer de Federico Garc�a Lorca 23,8170,MONTORN�S DEL VALL�S
+9100676061,PASSEIG DE LA FLORIDA 15,8130,SANTA PERP�TUA DE MOGODA
+9100743597,CARRER COLOM 209,8223,TERRASSA
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+9100745932,Carrer del Germ� Joaquim 55,8223,TERRASSA
+9100727035,Avinguda Nostra Senyora de Monts 22,8504,SANT JULI� DE VILATORTA
+9100723081,Carretera Seva 2,8554,SANT MIQUEL DE BALENYA
+9100666742,Carrer del Ti?ler 27,8503,GURB
+9100741999,Avinguda Catalunya 3,8105,SANT FOST DE CAMPSENTELLES
+9100736469,Carrer de Mil� 22,8917,BADALONA
+9100722593,Carrer Ramon Pujols Claveras 42,8510,RODA DE TER
+9100703787,Carrer Gran 15,8506,CALLDETENES
+9100703223,CASA NOVA DEL CASTELL S/N,8585,MONTESQUIU
+9100691332,Carrer del Mestre Quer 12,8580,SANT QUIRZE DE BESORA
+9100677847,AUTOVIA C-17 S/N,8580,SANT QUIRZE DE BESORA
+9100695204,Carrer Josep Vilanova 27,8572,SANT PERE DE TORELL�
+9100723139,CALLE DEL CASAL 1,8415,BIGUES I RIELLS
+9100737399,CALLE DE LA FABRICA 26,8187,SANTA EULALIA DE RON�ANA
+9100747990,Carrer de Concepci� Ducloux s/n,17500,RIPOLL
+9100723869,AVINGUDA DEL CASTELL 18,8570,TORELLO
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+9100682560,PASEO TER S/N,8585,MONTESQUIU
+9100758280,Carrer Rossell� 22,8521,BELLAVISTA
+9100628975,PASAJE MARTI S/N,8185,LLI�� DE VALL
+9100653354,Cam� de la Pineda 6,8186,LLI�� D'AMUNT
+9100653355,Passeig l'Alzina 1,8420,CANOVELLES
+9100659648,Carrer de Joanot Martorell 34,8403,GRANOLLERS
+9100662674,Passeig de la Conca de Bes�s 45,8403,GRANOLLERS
+9100666589,Carrer de la Ribera d'Ebre 56,8186,LLI�� D'AMUNT
+9100677459,Carrer Can Nadal 12,8185,LLI�� DE VALL
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+9100679516,Carrer de Folch i Torres 102,8186,LLI�� D'AMUNT
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+9100679906,Carrer de Barcelona 136,8105,SANT FOST DE CAMPSENTELLES
+9100681160,CALLE GALILEU GALILEI 16,8403,GRANOLLERS
+9100683566,Cam� del Gual 11,8187,SANTA EUL�LIA DE RON�ANA
+9100698329,PASAJE MARTI S/N,8185,LLI�� DE VALL
+9100717355,CALLE RAFAEL CASANOVA 6,8186,LLI�� D'AMUNT
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+9100729658,Carrer Raval 5,8185,PARETS DEL VALLES
+9100729943,Carrer Major 27,8160,MONTMEL�
+9100733355,Carrer Barcelona 60,8521,BELLAVISTA
+9100735422,CALLE VIC 7,8160,MONTMEL�
+9100735547,Pasaje Extremadura S/N,8521,LES FRANQUESES DEL VALLES
+9100736215,C-17 17,17500,RIPOLL
+9100736235,Carrer Pirineu S/N,8519,TAVERNOLES
+9100736259,Ctra. Parador Km. 7,8519,TAV�RNOLES
+9100736266,BP-4654 Km 1.2,8588,SORA
+9100736272,CARRER AJUNTAMENT 5,8571,SAN VICEN� DE TORELLO
+9100736411,Pla�a del Dr. Robert 1,17867,CAMPRODON
+9100736486,Carrer d'Europa 1,8913,BADALONA
+9100736670,Carrer Proven�a 29,8521,BELLAVISTA
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+9100742290,CARRETERA N-141 D S/N,8506,CALLDETENES
+9100743554,Avinguda del Mil�lenari s/n,8160,MONTMEL�
+9100743595,CARRER DE L'ANOIA 7,8192,SANT QUIRZE DEL VALL�S
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+9100745792,AVENIDA MIL.LENARI 12,8160,MONTMEL�
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+9100751813,CARRETERA OLESA S/N,8232,VILADECAVALLS
+9100756104,Carretera de Ribes 21,8585,MONTESQUIU
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+9100757467,CARRETERA LA ROCA S/N,8105,SANT FOST DE CAMPSENTELLE
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+9100759429,Carretera de Manlleu km 4,8503,GURB
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+9100675584,CALLE METALURGIA S/N,8186,LLI�A D�AMUNT
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+9100736185,N-260 104,17860,SANT JOAN DE LES ABADESSES
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+9100736227,CALLE SANT JAUME 25,17401,ARB�CIES
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+9100058963,"CARRETERA BALENYA KM 1,3",8552,TARADELL
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+9100059171,CALLE EL CANIGO 68,8570,TORELL�
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+9100411688,PASEO ANDALUSIA 9,8521,LES FRANQUESES DEL VALL�S
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+9100054889,PLAZA LA VILA 4,8105,SANT FOST DE CAMPSENTELLES
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+9100044002,SABADELL A GRANOLLERS KM 10.5,8185,LLI�A DE VALL
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+126089,Pla�a Augusta 3,8173,SANT CUGAT DEL VALL�S
+119956,POL. IND. PUIGTIO CARRER RIUDELLOTS,17412,MA�ANET DE LA SELVA
+9100190779,Avinguda de la Llibertat 39,8100,MOLLET DEL VALLES
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+9100336673,CALLE NICARAGUA 5,8100,MOLLET DEL VALLES
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+100423,"C/ PRAT DE LA RIBA, 13",8830,SANT BOI DE LLOBREGAT
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+128051,RAMBLA NOVA 10,8100,MOLLET DEL VALL�S
+9100498065,Avinguda de la Llibertat 20,8100,MOLLET DEL VALLES
+9100501076,PLAZA PORXADA 3,8401,GRANOLLERS
+9100502995,CALLE FRANSCESC RIBAS 64,8402,GRANOLLERS
+9100503205,CALLE ENRIC MORERA 27,8100,MOLLET DEL VALL�S
+9100503664,CALLE L'ALIAN�A 7,8186,LLI�� D'AMUNT
+9100529176,PLAZA ESPANYA 10,8521,BELLAVISTA
+9100529181,Avinguda d'Antoni Gaud� 3,8100,MOLLET DEL VALLES
+9100529300,CALLE PALAU D'AMETLLA 15,8170,MONTORN�S DEL VALL�S
+9100535260,CALLE JOSEP TORRAS I BAGES 50,8401,GRANOLLERS
+9100536475,RAMBLA NOVA 33-35,8100,MOLLET DEL VALL�S
+9100536484,AVENIDA L'ALZINAR 11,8033,BARCELONA
+9100536762,CALLE MAJOR 66,8170,MONTORN�S DEL VALL�S
+9100537742,Carrer Major 51,8160,MONTMEL�
+9100538933,CALLE GIRONA 12,8402,GRANOLLERS
+9100539485,Rambla Nova 3,8100,MOLLET DEL VALLES
+9100540782,AVENIDA RIVOLI 22,8100,MOLLET DEL VALL�S
+9100541197,Carrer de Can Puig S/N,8107,MARTORELLES
+9100542069,PERE RAMON S/N (DARRERA MERCAT S/N,8100,MOLLET DEL VALL�S
+9100544203,Carrer de Borrell 16,8100,MOLLET DEL VALLES
+9100544826,CALLE FRANCESC LAYRET 28,8100,MOLLET DEL VALL�S
+9100545628,BERENGUER III 71,8100,MOLLET DEL VALL�S
+9100547290,CALLE RIBERA D'EBRE 81,8186,LLI�� D'AMUNT
+9100550379,CALLE CAMP DE LES MORERES 2,8401,GRANOLLERS
+9100552330,Carrer d'Anselm Clav� 13,8100,MOLLET DEL VALLES
+9100553092,Carrer de Minetes 29,8402,GRANOLLERS
+9100553915,ZONA ESPORTIVA SN,8520,FRANQUESES DEL VALLES (LES)
+9100554598,AVENIDA SANT ESTEVE 3,8402,GRANOLLERS
+9100556448,CALLE BERENGUER III 168,8100,MOLLET DEL VALLES
+9100556449,AVENIDA ANTONI GAUDI 57,8100,MOLLET DEL VALL�S
+9100558804,PLAZA CORONA 4,8402,GRANOLLERS
+9100559291,CALLE CASTELLTER�OL 1-3,8401,GRANOLLERS
+9100560544,PASEO JOAN MIRO 8,8185,PARETS DEL VALLES
+9100562973,CALLE CASTELLTER�OL 15,8401,GRANOLLERS
+9100564349,Carrer de la Carrerada 5,8107,MARTORELLES
+9100566100,AVENIDA LLUIS COMPANYS 39,8150,PARETS DEL VALL�S
+9100566254,RAMBLA NOVA 29-31,8100,MOLLET DEL VALL�S
+9100568067,CALLE MATARO 6,8403,GRANOLLERS
+9100568310,PLAZA MAJOR 24,8100,MOLLET DEL VALLES
+9100569006,AVENIDA PEDRA DEL DIABLE 6,8150,PARETS DEL VALLES
+9100574514,AVENIDA CATALUNYA 24,8150,PARETS DEL VALL�S
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+9100582978,"RAMBLA NOVA, 69 -",8100,MOLLET DEL VALL�S
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+9100591127,AVENIDA DE LA LLIBERTAD 53,8100,MOLLET DEL VALL�S
+9100591222,CALLE ANSELM CLAVE 2,8186,LLI#A D'AMUNT
+9100591493,PASEO FLUVIAL S/N,8150,PARETS DEL VALL�S
+9100591545,PLAZA LA FONT 2,8186,LLI�� D'AMUNT
+9100596041,Pla�a de Prat de la Riba 18,8100,MOLLET DEL VALLES
+9100596801,CALLE MOLI DE CAN BASSA S/N,8100,MOLLET DEL VALL�S
+9100597581,AVENIDA CATALUNYA 14,8185,LLI�A DE VALL
+9100601682,BLAS INFANTE 15,8170,MONTORN�S DEL VALL�S
+9100601980,CALLE SANT ROC 17,8401,GRANOLLERS
+9100602372,CARRETERA CALDES 151,8186,LLI�A D'AMUNT
+9100603628,PASEO MUNTANYA 90,8402,GRANOLLERS
+9100607321,AVENIDA CALDES 27,8100,MOLLET DEL VALLES
+9100607527,PLAZA CAN MONIC 6,8402,GRANOLLERS
+9100645059,ESTACIO 2,8500,VIC
+9100645059,ESTACIO 2,8500,VIC
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+9100495381,AVENIDA EUROPA 21,8401,GRANOLLERS
+9100264285,DEL CALDERI S/N,8100,MOLLET DEL VALLES
+9100264285,DEL CALDERI S/N,8100,MOLLET DEL VALLES
+9100264285,DEL CALDERI S/N,8100,MOLLET DEL VALLES
+9100264285,DEL CALDERI S/N,8100,MOLLET DEL VALLES
+9100663630,Carrer de la Riera 25,8500,VIC
+9100722247,Carretera d'Olot 140,8560,MANLLEU
+9100569087,CALLE PRUIT S/N,8500,VIC
+9100749940,CALLE HORTA 11,8107,MARTORELLES

firestore_writer.py

@@ -0,0 +1,313 @@
+"""Push solver output into Firestore so the frontend reads it live.
+
+Document shape:
+
+    routes/{driverId}
+      driver_id, truck_id,
+      truck_layout   {rows, cols}                pallet floor grid
+      item_grid      {L, W, H}                   lattice cells (cube_size_m per van)
+      items          [{position {x,y,z},
+                       shape    {w_x, w_y, w_z},
+                       stop_index, product_id, is_returnable}]
+      points         [{lat, lng, address?}]      ordered route (depot at index 0)
+      pallets        [{row, col, products[]}]    legacy floor-only view
+      deliveries     [{pallet_positions[]}]      aligned with points
+      windows        [{start, end}]
+      service_times  [number]                    minutes per stop
+      delivery_status, status
+
+The Firebase Admin app is initialised lazily so the FastAPI process starts
+fine without credentials (e.g. local solver-only dev). When the
+`seed/service-account.json` is missing we log a warning and noop on writes.
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from pathlib import Path
+from typing import Optional
+
+_BASE = Path(__file__).resolve().parent
+_REPO = _BASE.parent
+_DEFAULT_CRED = _REPO / "seed" / "service-account.json"
+
+_db = None
+_init_attempted = False
+
+
+def _try_init() -> None:
+    """Lazy init. Credential precedence:
+      1. FIREBASE_SERVICE_ACCOUNT_JSON  — full JSON inline (best for serverless secrets)
+      2. FIREBASE_SERVICE_ACCOUNT       — path to a service-account.json on disk
+      3. seed/service-account.json      — legacy default for local dev
+    """
+    global _db, _init_attempted
+    if _init_attempted:
+        return
+    _init_attempted = True
+
+    try:
+        import firebase_admin
+        from firebase_admin import credentials, firestore
+    except ImportError as exc:
+        print(f"[firestore_writer] firebase_admin missing: {exc}")
+        return
+
+    cred = None
+    inline = os.environ.get("FIREBASE_SERVICE_ACCOUNT_JSON")
+    if inline:
+        try:
+            cred = credentials.Certificate(json.loads(inline))
+            print("[firestore_writer] loaded credentials from FIREBASE_SERVICE_ACCOUNT_JSON")
+        except Exception as exc:  # noqa: BLE001
+            print(f"[firestore_writer] FIREBASE_SERVICE_ACCOUNT_JSON parse failed: {exc}")
+
+    if cred is None:
+        cred_path = Path(os.environ.get("FIREBASE_SERVICE_ACCOUNT", _DEFAULT_CRED))
+        if not cred_path.exists():
+            print(f"[firestore_writer] no service account (env or {cred_path}) — writes disabled")
+            return
+        try:
+            cred = credentials.Certificate(str(cred_path))
+            print(f"[firestore_writer] loaded credentials from {cred_path}")
+        except Exception as exc:  # noqa: BLE001
+            print(f"[firestore_writer] cert load failed: {exc}")
+            return
+
+    try:
+        if not firebase_admin._apps:
+            firebase_admin.initialize_app(cred)
+        _db = firestore.client()
+        print("[firestore_writer] firestore client initialised")
+    except Exception as exc:  # noqa: BLE001
+        print(f"[firestore_writer] init failed: {exc}")
+
+
+def is_enabled() -> bool:
+    _try_init()
+    return _db is not None
+
+
+def _hm(s: int) -> str:
+    h, m = divmod(int(s) // 60, 60)
+    return f"{h:02d}:{m:02d}"
+
+
+def _van_layout(van_type: str) -> dict:
+    """Pallet floor layout (rows × cols) for the visual grid."""
+    return {"6_pallets": {"rows": 2, "cols": 3}, "8_pallets": {"rows": 2, "cols": 4}}.get(
+        van_type, {"rows": 2, "cols": 3}
+    )
+
+
+def _van_lattice(van_type: str) -> tuple[int, int, int]:
+    """Lattice dimensions in cells: each axis is van_dim_m / cube_size_m.
+
+    cube_size_m and physical interior dimensions both live in vans.json — keep
+    the source of truth there. Returns (L, W, H) where L is along the truck
+    length, W its depth, H its stacking height.
+    """
+    data = json.loads((_BASE / "vans.json").read_text(encoding="utf-8"))
+    spec = next((v for v in data["van_types"] if v["type"] == van_type), None)
+    if spec is None:
+        raise KeyError(f"unknown van_type: {van_type}")
+    cube = spec["cube_size_m"]
+    return (
+        int(round(spec["length_m"] / cube)),
+        int(round(spec["width_m"]  / cube)),
+        int(round(spec["height_m"] / cube)),
+    )
+
+
+def _compute_item_layout(
+    L: int, W: int, H: int,
+    deliveries_per_stop: list[tuple[int, list[str]]],
+    products: dict,
+) -> tuple[list[dict], dict]:
+    """Run SmartTruckOptimizer3D with real product shapes. Returns one entry
+    per *item* (not per cell): each carries an anchor `position` + box `shape`
+    so the frontend can render multi-cell products as single boxes.
+
+    `deliveries_per_stop` is an ordered list of (stop_index, [product_id per
+    unit]); the optimizer respects that route order so earlier stops are
+    extractable first. Every unit of an `is_returnable` product is created as
+    a return-type instance so the optimizer's EMPTY_KEG ablation models the
+    space empties leave behind.
+    """
+    counts_per_stop: dict[int, dict[str, int]] = {}
+    for stop_idx, units in deliveries_per_stop:
+        if not units:
+            continue
+        bucket: dict[str, int] = {}
+        for pid in units:
+            bucket[pid] = bucket.get(pid, 0) + 1
+        counts_per_stop[stop_idx] = bucket
+
+    if not counts_per_stop:
+        return [], {"L": L, "W": W, "H": H}
+
+    needed_pids = {pid for counts in counts_per_stop.values() for pid in counts}
+    item_shapes = {
+        pid: (
+            int(products[pid]["length_cells"]),
+            int(products[pid]["width_cells"]),
+            int(products[pid]["height_cells"]),
+        )
+        for pid in needed_pids
+    }
+
+    capacity = L * W * H
+    total_cells = sum(
+        item_shapes[pid][0] * item_shapes[pid][1] * item_shapes[pid][2] * cnt
+        for counts in counts_per_stop.values()
+        for pid, cnt in counts.items()
+    )
+    if total_cells > capacity:
+        # OR-tools VRP capacity already enforces this; defensive scaling so the
+        # lattice optimizer doesn't deadlock if a request slips through.
+        scale = capacity / total_cells
+        counts_per_stop = {
+            stop_idx: {pid: max(1, int(cnt * scale)) for pid, cnt in counts.items()}
+            for stop_idx, counts in counts_per_stop.items()
+        }
+
+    returns_per_stop = {
+        stop_idx: {pid: cnt for pid, cnt in counts.items() if products[pid]["is_returnable"]}
+        for stop_idx, counts in counts_per_stop.items()
+    }
+
+    route_ids = [stop_idx for stop_idx, _ in deliveries_per_stop if stop_idx in counts_per_stop]
+
+    import numpy as np  # noqa: F401  (used by SmartTruckOptimizer3D)
+    from optimize_box import SmartTruckOptimizer3D
+
+    opt = SmartTruckOptimizer3D(L, W, H, route_ids, item_shapes=item_shapes)
+    initial = opt.generate_initial_state(counts_per_stop, returns_per_stop)
+    final, _, _ = opt.optimize(initial, steps=2000)
+    opt._sync_instances_to_state(final)
+
+    items: list[dict] = []
+    for inst in opt._instances.values():
+        x, y, z = inst.anchor
+        lx, ly, lz = inst.shape
+        items.append({
+            "position": {"x": int(x), "y": int(y), "z": int(z)},
+            "shape": {"w_x": int(lx), "w_y": int(ly), "w_z": int(lz)},
+            "stop_index": int(inst.client),
+            "product_id": inst.item_type,
+            "is_returnable": bool(products[inst.item_type]["is_returnable"]),
+        })
+    return items, {"L": L, "W": W, "H": H}
+
+
+def _pallets_for_stop(stop: dict, products: dict, capacity_cells: int = 9) -> list[list[dict]]:
+    """Greedy packer: group a stop's deliveries into pallets capped at
+    `capacity_cells` cube-units each. One pallet's `products` list is in the
+    same shape the frontend already consumes."""
+    items: list[tuple[str, int]] = []
+    for line in stop.get("deliveries", []):
+        pid = line["product_id"]
+        cells = (
+            products[pid]["length_cells"]
+            * products[pid]["width_cells"]
+            * products[pid]["height_cells"]
+        )
+        items.extend([(pid, cells)] * line["qty"])
+
+    pallets: list[list[dict]] = []
+    current: dict[str, int] = {}
+    used = 0
+    for pid, cells in items:
+        if used + cells > capacity_cells and current:
+            pallets.append([{"product_id": p, "quantity": q} for p, q in current.items()])
+            current, used = {}, 0
+        current[pid] = current.get(pid, 0) + 1
+        used += cells
+    if current:
+        pallets.append([{"product_id": p, "quantity": q} for p, q in current.items()])
+    return pallets
+
+
+def build_route_doc(
+    *,
+    driver_id: str,
+    truck_id: str,
+    van_type: str,
+    depot: dict,
+    request_stops: list[dict],
+    van_plan,
+    service_times_s: dict[str, float],
+) -> dict:
+    """Translate one VanPlan into the Firestore route document shape."""
+    layout = _van_layout(van_type)
+    rows, cols = layout["rows"], layout["cols"]
+    L, W, H = _van_lattice(van_type)
+    products = {p["id"]: p for p in json.loads((_BASE / "products.json").read_text())["products"]}
+    by_id = {s["id"]: s for s in request_stops}
+
+    points: list[dict] = [
+        {"lat": depot["coords"]["lat"], "lng": depot["coords"]["lng"], "address": depot.get("id", "Depot")}
+    ]
+    windows: list[dict] = [{"start": depot["open"], "end": depot["close"]}]
+    service_times_min: list[float] = [0]
+    pallets: list[dict] = []
+    deliveries: list[dict] = [{"pallet_positions": []}]
+
+    next_slot = 0  # row-major fill of the truck floor
+    deliveries_per_stop: list[tuple[int, list[str]]] = []
+
+    for stop_idx, sp in enumerate(van_plan.stops, start=1):
+        s = by_id[sp.id]
+        points.append({
+            "lat": s["coords"]["lat"],
+            "lng": s["coords"]["lng"],
+            "address": s.get("address", s["id"]),
+        })
+        windows.append({"start": s["time_window"]["open"], "end": s["time_window"]["close"]})
+        service_times_min.append(round(service_times_s.get(sp.id, 0) / 60, 1))
+
+        stop_pallets = _pallets_for_stop(s, products)
+        positions: list[dict] = []
+        for pdata in stop_pallets:
+            if next_slot >= rows * cols:
+                break
+            r, c = divmod(next_slot, cols)
+            pallets.append({"row": r, "col": c, "products": pdata})
+            positions.append({"row": r, "col": c})
+            next_slot += 1
+        deliveries.append({"pallet_positions": positions})
+
+        units: list[str] = []
+        for line in s.get("deliveries", []):
+            units.extend([line["product_id"]] * line["qty"])
+        deliveries_per_stop.append((stop_idx, units))
+
+    items, item_grid = _compute_item_layout(L, W, H, deliveries_per_stop, products)
+
+    return {
+        "driver_id": driver_id,
+        "truck_id": truck_id,
+        "truck_layout": layout,
+        "item_grid": item_grid,
+        "items": items,
+        "points": points,
+        "pallets": pallets,
+        "deliveries": deliveries,
+        "windows": windows,
+        "service_times": service_times_min,
+        "delivery_status": ["pending"] * len(points),
+        "status": "pending",
+    }
+
+
+def write_routes(docs: list[dict]) -> int:
+    """Write a batch of route docs to `routes/{driver_id}`. Returns count
+    written; 0 when Firestore is disabled."""
+    if not is_enabled():
+        return 0
+    written = 0
+    for doc in docs:
+        _db.collection("routes").document(doc["driver_id"]).set(doc)
+        written += 1
+    return written

geometry.py

@@ -0,0 +1,54 @@
+"""Trace each van's route along actual OSM roads, returning a polyline.
+
+Used to render routes on the frontend map as curves following the streets,
+not as straight chord lines between stops.
+"""
+
+from __future__ import annotations
+
+import networkx as nx
+
+from travel_time import TravelMatrix
+
+
+def _edge_coords(graph: nx.MultiDiGraph, u: int, v: int) -> list[tuple[float, float]]:
+    """Coords along the (u, v) edge. Uses curve geometry when present."""
+    data = graph.get_edge_data(u, v)
+    if data:
+        first = next(iter(data.values()))
+        geom = first.get("geometry")
+        if geom is not None:
+            xs, ys = geom.xy
+            return [(float(y), float(x)) for x, y in zip(xs, ys)]
+    return [
+        (float(graph.nodes[u]["y"]), float(graph.nodes[u]["x"])),
+        (float(graph.nodes[v]["y"]), float(graph.nodes[v]["x"])),
+    ]
+
+
+def route_polyline(
+    graph: nx.MultiDiGraph,
+    matrix: TravelMatrix,
+    stop_sequence: list[str],
+) -> list[tuple[float, float]]:
+    """Concatenate OSM-traced legs into one (lat, lng) polyline.
+
+    `stop_sequence` typically starts and ends with "DEPOT", e.g.
+    ["DEPOT", "S005", "S007", "DEPOT"].
+    """
+    out: list[tuple[float, float]] = []
+    for src_id, dst_id in zip(stop_sequence, stop_sequence[1:]):
+        src_node = matrix.node_ids[matrix.index_of(src_id)]
+        dst_node = matrix.node_ids[matrix.index_of(dst_id)]
+        try:
+            path = nx.shortest_path(graph, src_node, dst_node, weight="travel_time")
+        except nx.NetworkXNoPath:
+            continue
+        for u, v in zip(path, path[1:]):
+            seg = _edge_coords(graph, u, v)
+            # Avoid duplicating the joint node at the boundary between legs.
+            if out and seg and out[-1] == seg[0]:
+                out.extend(seg[1:])
+            else:
+                out.extend(seg)
+    return out

graph_manager.py

@@ -0,0 +1,251 @@
+"""Build and persist a road graph covering Granollers and Mollet del Vallès.
+
+Within each city we keep the full drivable network so deliveries can be routed
+to any address. Between the cities we only keep major roads (motorway, trunk,
+primary) since the truck just needs the fastest corridor from one to the other.
+"""
+
+from __future__ import annotations
+
+import csv
+import json
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import networkx as nx
+import osmnx as ox
+from shapely.ops import unary_union
+
+GRANOLLERS = "Granollers, Catalunya, Spain"
+MOLLET_DEL_VALLES = "Mollet del Vallès, Catalunya, Spain"
+
+NETWORK_TYPE = "drive"
+
+# Inter-city corridor: only fast roads. Includes link ramps so the corridor
+# actually connects to the local networks at on/off-ramps.
+MAJOR_ROAD_FILTER = (
+    '["highway"~"motorway|trunk|primary|'
+    'motorway_link|trunk_link|primary_link"]'
+)
+
+DEFAULT_GRAPH_PATH = Path(__file__).with_name("cities_graph.graphml")
+DEFAULT_COORDS_PATH = Path(__file__).with_name("coords.csv")
+DEFAULT_REQUEST_PATH = Path(__file__).with_name("data") / "sample_request.json"
+
+
+def load_depot_coords(path: Path | str = DEFAULT_REQUEST_PATH) -> tuple[float, float] | None:
+    """Return (lat, lng) of the depot if `sample_request.json` exists, else None."""
+    path = Path(path)
+    if not path.exists():
+        return None
+    req = json.loads(path.read_text(encoding="utf-8"))
+    c = req.get("depot", {}).get("coords")
+    if not c:
+        return None
+    return float(c["lat"]), float(c["lng"])
+
+
+def _draw_depot(ax, depot: tuple[float, float] | None) -> None:
+    if depot is None:
+        return
+    lat, lng = depot
+    ax.scatter(
+        [lng], [lat],
+        s=160, c="#d62728", marker="*", zorder=10,
+        edgecolors="black", linewidth=1.0,
+        label="Warehouse",
+    )
+
+
+def _city_graph(place: str) -> nx.MultiDiGraph:
+    return ox.graph_from_place(place, network_type=NETWORK_TYPE)
+
+
+def _corridor_graph(places: list[str]) -> nx.MultiDiGraph:
+    """Major-roads-only graph spanning the bounding box of the given places."""
+    boundaries = [ox.geocode_to_gdf(p).geometry.iloc[0] for p in places]
+    minx, miny, maxx, maxy = unary_union(boundaries).bounds
+    return ox.graph_from_bbox(
+        bbox=(minx, miny, maxx, maxy),
+        custom_filter=MAJOR_ROAD_FILTER,
+        truncate_by_edge=True,
+        retain_all=False,
+    )
+
+
+def build_combined_graph() -> nx.MultiDiGraph:
+    """Return a single graph with both cities plus the minimal corridor between."""
+    g_granollers = _city_graph(GRANOLLERS)
+    g_mollet = _city_graph(MOLLET_DEL_VALLES)
+    g_corridor = _corridor_graph([GRANOLLERS, MOLLET_DEL_VALLES])
+
+    combined = nx.compose_all([g_granollers, g_mollet, g_corridor])
+    # Preserve graph-level metadata (CRS) that compose_all drops.
+    combined.graph.update(g_granollers.graph)
+
+    # Drop disconnected fragments left over after composition.
+    combined = ox.truncate.largest_component(combined, strongly=True)
+
+    # Enrich edges with speed_kph + travel_time (seconds) so downstream
+    # routing can use realistic times instead of raw distance.
+    combined = ox.routing.add_edge_speeds(combined)
+    combined = ox.routing.add_edge_travel_times(combined)
+    return combined
+
+
+def save_graph(graph: nx.MultiDiGraph, path: Path | str = DEFAULT_GRAPH_PATH) -> Path:
+    path = Path(path)
+    ox.save_graphml(graph, path)
+    return path
+
+
+def load_graph(path: Path | str = DEFAULT_GRAPH_PATH) -> nx.MultiDiGraph:
+    return ox.load_graphml(Path(path))
+
+
+def get_or_build_graph(path: Path | str = DEFAULT_GRAPH_PATH) -> nx.MultiDiGraph:
+    path = Path(path)
+    if path.exists():
+        return load_graph(path)
+    graph = build_combined_graph()
+    save_graph(graph, path)
+    return graph
+
+
+def load_dropoff_coords(path: Path | str = DEFAULT_COORDS_PATH) -> list[tuple[float, float]]:
+    """Read drop-off coordinates from a CSV with columns `id, x, y` (x=lng, y=lat)."""
+    points: list[tuple[float, float]] = []
+    with open(path, newline="", encoding="utf-8") as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            points.append((float(row["y"]), float(row["x"])))
+    return points
+
+
+def _edge_colors(graph: nx.MultiDiGraph) -> list[str]:
+    keywords = ("motorway", "trunk", "primary")
+    # Iterate with keys=True so the list aligns 1:1 with osmnx's internal
+    # edge ordering in MultiDiGraph plotting.
+    return [
+        "#d62728" if any(k in str(data.get("highway", "")) for k in keywords) else "#444444"
+        for _, _, _, data in graph.edges(keys=True, data=True)
+    ]
+
+
+def plot_graph(
+    graph: nx.MultiDiGraph,
+    save_path: Path | str | None = None,
+    depot: tuple[float, float] | None = None,
+) -> Path | None:
+    """Show the graph with city streets in grey and the inter-city corridor in red.
+
+    Always writes a PNG (default `cities_graph.png` next to this file) because the
+    interactive tkagg window can fail to render edges on some Windows setups —
+    the file is the source of truth.
+    """
+    fig, ax = ox.plot_graph(
+        graph,
+        figsize=(12, 12),
+        edge_color=_edge_colors(graph),
+        edge_linewidth=1.2,
+        edge_alpha=1.0,
+        node_size=3,
+        node_color="#1f77b4",
+        bgcolor="white",
+        show=False,
+        close=False,
+    )
+    _draw_depot(ax, depot)
+    if depot is not None:
+        ax.legend(loc="upper right", framealpha=0.9)
+    ax.set_title(
+        f"Granollers + Mollet del Vallès "
+        f"({graph.number_of_nodes()} nodes, {graph.number_of_edges()} edges)"
+    )
+    out = Path(save_path) if save_path else Path(__file__).with_name("cities_graph.png")
+    fig.savefig(out, dpi=120, bbox_inches="tight")
+    plt.show()
+    return out
+
+
+def plot_graph_with_dropoffs(
+    graph: nx.MultiDiGraph,
+    coords: list[tuple[float, float]],
+    title: str | None = None,
+    save_path: Path | str | None = None,
+    depot: tuple[float, float] | None = None,
+) -> Path | None:
+    """Overlay drop-off coordinates on the road graph.
+
+    `coords` is a list of (lat, lng) pairs (matches `load_dropoff_coords`).
+    Axis limits expand to include any drop-offs that fall outside the graph
+    extent so off-area points are still visible (they may indicate the graph
+    needs to be rebuilt over a different region).
+    """
+    fig, ax = ox.plot_graph(
+        graph,
+        figsize=(12, 12),
+        edge_color=_edge_colors(graph),
+        edge_linewidth=1.2,
+        edge_alpha=1.0,
+        node_size=3,
+        node_color="#1f77b4",
+        bgcolor="white",
+        show=False,
+        close=False,
+    )
+    pad = 0.005
+    node_lngs = [graph.nodes[n]["x"] for n in graph.nodes()]
+    node_lats = [graph.nodes[n]["y"] for n in graph.nodes()]
+    xmin, xmax = min(node_lngs) - pad, max(node_lngs) + pad
+    ymin, ymax = min(node_lats) - pad, max(node_lats) + pad
+
+    inside = [(lat, lng) for lat, lng in coords if xmin <= lng <= xmax and ymin <= lat <= ymax]
+    outside_count = len(coords) - len(inside)
+    if outside_count:
+        print(
+            f"warning: {outside_count}/{len(coords)} drop-offs fall outside the "
+            f"graph extent and were cropped from the plot"
+        )
+
+    lats = [c[0] for c in inside]
+    lngs = [c[1] for c in inside]
+    ax.scatter(
+        lngs, lats,
+        s=10, c="#2ca02c", alpha=0.7, zorder=5,
+        edgecolors="white", linewidth=0.3,
+        label=f"{len(inside)} drop-offs (in extent)",
+    )
+
+    _draw_depot(ax, depot)
+
+    ax.set_xlim(xmin, xmax)
+    ax.set_ylim(ymin, ymax)
+    ax.legend(loc="upper right", framealpha=0.9)
+    ax.set_title(
+        title
+        or f"Graph + {len(coords)} drop-offs "
+           f"({graph.number_of_nodes()} nodes, {graph.number_of_edges()} edges)"
+    )
+    out = Path(save_path) if save_path else Path(__file__).with_name("cities_graph_dropoffs.png")
+    fig.savefig(out, dpi=120, bbox_inches="tight")
+    plt.show()
+    return out
+
+
+if __name__ == "__main__":
+    g = build_combined_graph()
+    print(f"Nodes: {g.number_of_nodes()}  Edges: {g.number_of_edges()}")
+    out = save_graph(g)
+    print(f"Saved to {out}")
+
+    depot = load_depot_coords()
+    if depot is not None:
+        print(f"Depot at lat={depot[0]:.4f}, lng={depot[1]:.4f}")
+    if DEFAULT_COORDS_PATH.exists():
+        coords = load_dropoff_coords(DEFAULT_COORDS_PATH)
+        print(f"Loaded {len(coords)} drop-off points from {DEFAULT_COORDS_PATH.name}")
+        png = plot_graph_with_dropoffs(g, coords, depot=depot)
+    else:
+        png = plot_graph(g, depot=depot)
+    print(f"Saved plot to {png}")

interhack26.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c96049f4b662f1bd4a438ff514cb706de41b77ae4af9282e0e1a321b1fb5b9cb
+size 154643

loader.py

@@ -0,0 +1,136 @@
+"""Parse a request dict + product/van catalogues into solver inputs.
+
+The shape of the request dict matches `sample_request.json`. The catalogues
+(`products.json`, `vans.json`) are read from disk lazily — keeps startup
+cheap and avoids globals.
+"""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from pathlib import Path
+
+_BASE = Path(__file__).parent
+DEFAULT_PRODUCTS = _BASE / "products.json"
+DEFAULT_VANS = _BASE / "vans.json"
+
+SERVICE_BASE_S = 180.0          # parking + paperwork buffer per stop
+SERVICE_PER_CELL_S = 10.0       # crate-handling time per unit cell
+
+
+@dataclass
+class Depot:
+    id: str
+    lat: float
+    lng: float
+    open_s: int
+    close_s: int
+
+
+@dataclass
+class Driver:
+    id: str
+    shift_start_s: int
+    shift_end_s: int
+
+
+@dataclass
+class Fleet:
+    num_vans: int
+    capacity_kg: float
+    capacity_cells: int
+
+
+@dataclass
+class Stop:
+    id: str
+    lat: float
+    lng: float
+    t_open_s: int
+    t_close_s: int
+    delivery_cells: int
+    delivery_kg: float
+    pickup_cells: int
+    pickup_kg: float
+    service_time_s: float
+
+
+def _hms(s: str) -> int:
+    h, m = s.split(":")
+    return int(h) * 3600 + int(m) * 60
+
+
+def _van_capacity_cells(spec: dict) -> int:
+    cube = spec["cube_size_m"]
+    return (
+        int(round(spec["length_m"] / cube))
+        * int(round(spec["width_m"] / cube))
+        * int(round(spec["height_m"] / cube))
+    )
+
+
+def _van_spec(vans_path: Path, van_type: str) -> dict:
+    data = json.loads(vans_path.read_text(encoding="utf-8"))
+    for v in data["van_types"]:
+        if v["type"] == van_type:
+            return v
+    raise KeyError(f"unknown van_type: {van_type}")
+
+
+def _line_cells(p: dict, qty: int) -> int:
+    return p["length_cells"] * p["width_cells"] * p["height_cells"] * qty
+
+
+def load(
+    req: dict,
+    products_path: Path = DEFAULT_PRODUCTS,
+    vans_path: Path = DEFAULT_VANS,
+) -> tuple[Depot, Fleet, list[Driver], list[Stop]]:
+    products = {
+        p["id"]: p
+        for p in json.loads(products_path.read_text(encoding="utf-8"))["products"]
+    }
+    spec = _van_spec(vans_path, req["fleet"]["van_type"])
+
+    depot = Depot(
+        id=req["depot"]["id"],
+        lat=req["depot"]["coords"]["lat"],
+        lng=req["depot"]["coords"]["lng"],
+        open_s=_hms(req["depot"]["open"]),
+        close_s=_hms(req["depot"]["close"]),
+    )
+    fleet = Fleet(
+        num_vans=req["fleet"]["num_vans"],
+        capacity_kg=float(spec["max_payload_kg"]),
+        capacity_cells=_van_capacity_cells(spec),
+    )
+    drivers = [
+        Driver(d["id"], _hms(d["shift_start"]), _hms(d["shift_end"]))
+        for d in req["drivers"]
+    ]
+
+    stops: list[Stop] = []
+    for s in req["stops"]:
+        d_cells = sum(_line_cells(products[ln["product_id"]], ln["qty"]) for ln in s["deliveries"])
+        d_kg = sum(products[ln["product_id"]]["weight_kg"] * ln["qty"] for ln in s["deliveries"])
+        p_cells = sum(_line_cells(products[ln["product_id"]], ln["qty"]) for ln in s.get("pickups", []))
+        p_kg = sum(products[ln["product_id"]]["weight_kg"] * ln["qty"] for ln in s.get("pickups", []))
+        stops.append(Stop(
+            id=s["id"],
+            lat=s["coords"]["lat"], lng=s["coords"]["lng"],
+            t_open_s=_hms(s["time_window"]["open"]),
+            t_close_s=_hms(s["time_window"]["close"]),
+            delivery_cells=d_cells, delivery_kg=d_kg,
+            pickup_cells=p_cells, pickup_kg=p_kg,
+            service_time_s=SERVICE_BASE_S + SERVICE_PER_CELL_S * (d_cells + p_cells),
+        ))
+    return depot, fleet, drivers, stops
+
+
+def build_travel_matrix(depot: Depot, stops: list[Stop]):
+    """Travel-time + distance matrix scoped to depot + the request's stops."""
+    from travel_time import build_matrix
+    from graph_manager import get_or_build_graph
+    points = [("DEPOT", depot.lat, depot.lng)] + [(s.id, s.lat, s.lng) for s in stops]
+    return build_matrix(get_or_build_graph(), points)

optimize_box.py

@@ -0,0 +1,572 @@
+import numpy as np
+import random
+import math
+
+
+class _Instance:
+    """A multi-cell rectangular item placed in the truck lattice."""
+    __slots__ = ("id", "client", "item_type", "is_return", "anchor", "shape")
+
+    def __init__(self, instance_id, client, item_type, is_return, anchor, shape):
+        self.id = instance_id
+        self.client = client
+        self.item_type = item_type
+        self.is_return = is_return
+        self.anchor = anchor      # (x, y, z) min corner
+        self.shape = shape        # (lx, ly, lz)
+
+    def cells_at(self, anchor):
+        x0, y0, z0 = anchor
+        lx, ly, lz = self.shape
+        out = []
+        for i in range(lx):
+            for j in range(ly):
+                for k in range(lz):
+                    out.append((x0 + i, y0 + j, z0 + k))
+        return out
+
+    @property
+    def cells(self):
+        return self.cells_at(self.anchor)
+
+
+class SmartTruckOptimizer3D:
+    EMPTY_KEG = -1  # post-return solid block; structural support but always blocks extraction
+
+    def __init__(self, length_bays, width_pallets, height_layers, route, item_shapes=None):
+        """
+        length_bays:    lateral bays (X-axis)
+        width_pallets:  depth of each bay (Y-axis); both Y-walls are open along the full length
+        height_layers:  max stacking height (Z-axis)
+        route:          client IDs in delivery order
+        item_shapes:    dict[item_type, (lx, ly, lz)] of rectangular box dimensions.
+                        Defaults to {"keg": (1, 1, 1)} for backward compatibility.
+
+        Removability rule: an instance is removable iff at least one of its three sides
+        — top, left (-Y), right (+Y) — is fully clear of blockers (other-client items
+        or empty kegs). The truck has no front/back access.
+        """
+        self.L = length_bays
+        self.W = width_pallets
+        self.H = height_layers
+        self.route = list(route)
+
+        shapes = item_shapes if item_shapes is not None else {"keg": (1, 1, 1)}
+        self.item_shapes = {}
+        for name, dims in shapes.items():
+            dims = tuple(int(d) for d in dims)
+            if len(dims) != 3 or any(d < 1 for d in dims):
+                raise ValueError(f"Invalid shape for item '{name}': {dims}")
+            if dims[0] > self.L or dims[1] > self.W or dims[2] > self.H:
+                raise ValueError(f"Item '{name}' shape {dims} exceeds grid {(self.L, self.W, self.H)}.")
+            self.item_shapes[name] = dims
+
+        self._instances = {}
+        self._next_id = 1
+
+        # Work caches (variance over cell-x; multi-cell items contribute each cell once)
+        self._x_sum = {}
+        self._x_sq_sum = {}
+        self._counts = {}
+        self._max_var = max(((self.L - 1) ** 2) / 4.0, 1e-9)
+
+    # ---------- helpers ----------
+
+    def _is_blocker(self, value, client):
+        """True iff a cell with `value` blocks `client` from extracting through it."""
+        if value == 0:
+            return False
+        if value == self.EMPTY_KEG:
+            return True
+        return self._instances[value].client != client
+
+    # ---------- initial state ----------
+
+    def generate_initial_state(self, client_item_counts, client_returns):
+        """
+        client_item_counts: {client_id: {item_type: total_count}}
+        client_returns:     {client_id: {item_type: returns_count}}
+
+        For each (client, item_type) we create `total - returns` pure-delivery instances
+        and `returns` substitution instances. Instances are placed at random valid
+        anchors; raises if the grid is too crowded.
+        """
+        self._instances = {}
+        self._next_id = 1
+        state = np.zeros((self.L, self.W, self.H), dtype=int)
+
+        pending = []  # (client, item_type, is_return, shape)
+        for client, items in client_item_counts.items():
+            returns_for_client = client_returns.get(client, {})
+            for item_type, count in items.items():
+                if item_type not in self.item_shapes:
+                    raise KeyError(f"Unknown item_type '{item_type}' (not in item_shapes).")
+                num_returns = returns_for_client.get(item_type, 0)
+                num_pure = count - num_returns
+                if num_pure < 0:
+                    raise ValueError(
+                        f"Client {client} item '{item_type}': returns ({num_returns}) "
+                        f"exceed total ({count})."
+                    )
+                shape = self.item_shapes[item_type]
+                for _ in range(num_pure):
+                    pending.append((client, item_type, False, shape))
+                for _ in range(num_returns):
+                    pending.append((client, item_type, True, shape))
+
+        total_cells_needed = sum(s[0] * s[1] * s[2] for *_, s in pending)
+        capacity = self.L * self.W * self.H
+        if total_cells_needed > capacity:
+            raise ValueError(f"Items need {total_cells_needed} cells; grid holds {capacity}.")
+
+        random.shuffle(pending)
+        for client, item_type, is_return, shape in pending:
+            anchor = self._place_random(state, shape)
+            if anchor is None:
+                raise RuntimeError(
+                    f"Could not place item '{item_type}' for client {client}; grid too crowded."
+                )
+            inst_id = self._next_id
+            self._next_id += 1
+            inst = _Instance(inst_id, client, item_type, is_return, anchor, shape)
+            self._instances[inst_id] = inst
+            for (x, y, z) in inst.cells:
+                state[x, y, z] = inst_id
+        return state
+
+    def _place_random(self, state, shape, max_tries=500):
+        lx, ly, lz = shape
+        for _ in range(max_tries):
+            x = random.randint(0, self.L - lx)
+            y = random.randint(0, self.W - ly)
+            z = random.randint(0, self.H - lz)
+            if self._cells_clear(state, x, y, z, lx, ly, lz):
+                return (x, y, z)
+        # Deterministic fallback scan
+        for x in range(self.L - lx + 1):
+            for y in range(self.W - ly + 1):
+                for z in range(self.H - lz + 1):
+                    if self._cells_clear(state, x, y, z, lx, ly, lz):
+                        return (x, y, z)
+        return None
+
+    def _cells_clear(self, state, x, y, z, lx, ly, lz, allow_id=0):
+        """True iff the box at (x,y,z) of size (lx,ly,lz) contains only 0 or `allow_id`."""
+        for i in range(lx):
+            for j in range(ly):
+                for k in range(lz):
+                    v = state[x + i, y + j, z + k]
+                    if v != 0 and v != allow_id:
+                        return False
+        return True
+
+    # ---------- penalty (full eval) ----------
+
+    def physical_penalty(self, state):
+        """
+        Sum of:
+          1. Initial gravity violations (cells whose support is void)
+          2. For each client in route order:
+              a. Extraction violations: instances with no fully-clear side
+              b. Post-ablation gravity violations
+        """
+        penalty = 0
+        truck = np.copy(state)
+
+        penalty += self._gravity_violations(truck)
+
+        # Group instances by client once
+        by_client = {c: [] for c in self.route}
+        for inst in self._instances.values():
+            if inst.client in by_client:
+                by_client[inst.client].append(inst)
+
+        for client in self.route:
+            for inst in by_client[client]:
+                if self._instance_blocked(truck, inst):
+                    penalty += 1
+            for inst in by_client[client]:
+                fill = self.EMPTY_KEG if inst.is_return else 0
+                for (x, y, z) in inst.cells:
+                    truck[x, y, z] = fill
+            penalty += self._gravity_violations(truck)
+
+        return penalty
+
+    def _gravity_violations(self, truck):
+        if self.H < 2:
+            return 0
+        upper = truck[:, :, 1:]
+        lower = truck[:, :, :-1]
+        return int(np.sum((upper > 0) & (lower == 0)))
+
+    def _instance_blocked(self, truck, inst):
+        """True iff none of {top, left, right} sides is fully clear of blockers."""
+        x0, y0, z0 = inst.anchor
+        lx, ly, lz = inst.shape
+        c = inst.client
+
+        # Top side: above the item
+        if z0 + lz >= self.H:
+            return False  # extends to roof, top is open by definition
+        if self._region_clear(truck, x0, x0 + lx, y0, y0 + ly, z0 + lz, self.H, c):
+            return False
+
+        # Left side
+        if y0 == 0:
+            return False
+        if self._region_clear(truck, x0, x0 + lx, 0, y0, z0, z0 + lz, c):
+            return False
+
+        # Right side
+        if y0 + ly >= self.W:
+            return False
+        if self._region_clear(truck, x0, x0 + lx, y0 + ly, self.W, z0, z0 + lz, c):
+            return False
+
+        return True
+
+    def _region_clear(self, truck, x_lo, x_hi, y_lo, y_hi, z_lo, z_hi, client):
+        for x in range(x_lo, x_hi):
+            for y in range(y_lo, y_hi):
+                for z in range(z_lo, z_hi):
+                    if self._is_blocker(truck[x, y, z], client):
+                        return False
+        return True
+
+    # ---------- delta penalty ----------
+
+    def _instances_reading_cells(self, changed_cells):
+        """Set of instance IDs whose blocking computation reads any of `changed_cells`."""
+        affected = set()
+        for inst_id, inst in self._instances.items():
+            x0, y0, z0 = inst.anchor
+            lx, ly, lz = inst.shape
+            for (cx, cy, cz) in changed_cells:
+                in_x = x0 <= cx < x0 + lx
+                in_y = y0 <= cy < y0 + ly
+                in_z = z0 <= cz < z0 + lz
+                if in_x and (
+                    (in_y and cz >= z0 + lz) or            # top column
+                    (cy < y0 and in_z) or                  # left row
+                    (cy >= y0 + ly and in_z) or            # right row
+                    (in_y and in_z)                        # inside (shouldn't happen unless self)
+                ):
+                    affected.add(inst_id)
+                    break
+        return affected
+
+    def _evaluate_local_penalty(self, state, changed_cells, affected_ids):
+        """
+        Sum penalty contributions whose values can differ between current and proposed
+        states. Cells outside `changed_cells` and instances outside `affected_ids` give
+        identical contributions in both, so they cancel in the delta.
+
+        The cascade still mutates a full-state copy (ablation is global), but only the
+        listed instances' extraction blocks and only the gravity neighbors of changed
+        cells contribute to the returned sum.
+        """
+        truck = np.copy(state)
+
+        gravity_cells = set()
+        for (x, y, z) in changed_cells:
+            gravity_cells.add((x, y, z))
+            if z + 1 < self.H:
+                gravity_cells.add((x, y, z + 1))
+
+        penalty = 0
+        for (x, y, z) in gravity_cells:
+            if z >= 1 and truck[x, y, z] > 0 and truck[x, y, z - 1] == 0:
+                penalty += 1
+
+        by_client = {c: [] for c in self.route}
+        for inst in self._instances.values():
+            if inst.client in by_client:
+                by_client[inst.client].append(inst)
+
+        for client in self.route:
+            for inst in by_client[client]:
+                if inst.id in affected_ids and self._instance_blocked(truck, inst):
+                    penalty += 1
+            for inst in by_client[client]:
+                fill = self.EMPTY_KEG if inst.is_return else 0
+                for (x, y, z) in inst.cells:
+                    truck[x, y, z] = fill
+            for (x, y, z) in gravity_cells:
+                if z >= 1 and truck[x, y, z] > 0 and truck[x, y, z - 1] == 0:
+                    penalty += 1
+
+        return penalty
+
+    # ---------- work ----------
+
+    def spatial_work(self, state):
+        """
+        Mean-normalized variance of x-coords per client, in roughly [0, 1].
+        Each cell of a multi-cell item contributes its x once; rewards keeping a
+        client's items clustered along the truck length so the driver walks less
+        per stop. Translation-invariant (cluster *position* doesn't matter — both
+        Y-sides are open, see project notes).
+        """
+        if not self.route:
+            return 0.0
+        total = 0.0
+        for client in self.route:
+            xs = []
+            for inst in self._instances.values():
+                if inst.client == client:
+                    x0, _, _ = inst.anchor
+                    lx, ly, lz = inst.shape
+                    for i in range(lx):
+                        xs.extend([x0 + i] * (ly * lz))
+            if len(xs) > 1:
+                total += float(np.var(xs))
+        return total / (self._max_var * len(self.route))
+
+    def _init_work_cache(self, state):
+        self._x_sum = {c: 0 for c in self.route}
+        self._x_sq_sum = {c: 0 for c in self.route}
+        self._counts = {c: 0 for c in self.route}
+        for inst in self._instances.values():
+            if inst.client not in self._counts:
+                continue
+            for (x, _, _) in inst.cells:
+                self._x_sum[inst.client] += x
+                self._x_sq_sum[inst.client] += x * x
+                self._counts[inst.client] += 1
+
+    def _work_from_cache(self):
+        if not self.route:
+            return 0.0
+        total = 0.0
+        for client in self.route:
+            n = self._counts.get(client, 0)
+            if n > 1:
+                mean = self._x_sum[client] / n
+                total += self._x_sq_sum[client] / n - mean * mean
+        return total / (self._max_var * len(self.route))
+
+    def _update_work_cache_for_move(self, inst, old_anchor, new_anchor):
+        """Reflect that `inst` translated from old_anchor to new_anchor. O(|cells|)."""
+        if inst.client not in self._counts:
+            return
+        for (x, _, _) in inst.cells_at(old_anchor):
+            self._x_sum[inst.client] -= x
+            self._x_sq_sum[inst.client] -= x * x
+        for (x, _, _) in inst.cells_at(new_anchor):
+            self._x_sum[inst.client] += x
+            self._x_sq_sum[inst.client] += x * x
+        # counts unchanged
+
+    # ---------- optimize ----------
+
+    def optimize(self, initial_state, steps=40000, seed=None,
+                 T_0=1.0, T_min=1e-4, gamma_0=0.01, gamma_max=100.0):
+        """
+        SA with single-instance translation proposals + delta penalty evaluation.
+
+        Each step picks a random instance, picks a random in-bounds anchor, and
+        accepts/rejects a translation by Metropolis criterion on H = W + γP. Same-anchor
+        proposals and proposals that overlap other items are skipped without scoring.
+        """
+        if seed is not None:
+            random.seed(seed)
+            np.random.seed(seed)
+
+        current_state = np.copy(initial_state)
+        self._init_work_cache(current_state)
+        current_P = self.physical_penalty(current_state)
+        current_W = self._work_from_cache()
+        current_H = current_W + gamma_max * current_P
+
+        best_state = np.copy(current_state)
+        best_H = current_H
+        best_feasible_state = None
+        best_feasible_W = float('inf')
+
+        instance_ids = list(self._instances.keys())
+        if not instance_ids:
+            return current_state, current_P, current_W
+
+        for step in range(steps):
+            fraction = step / float(steps)
+            T = T_0 * ((T_min / T_0) ** fraction)
+            gamma = gamma_0 + (gamma_max - gamma_0) * (fraction ** 4)
+
+            inst_id = random.choice(instance_ids)
+            inst = self._instances[inst_id]
+            lx, ly, lz = inst.shape
+
+            x_new = random.randint(0, self.L - lx)
+            y_new = random.randint(0, self.W - ly)
+            z_new = random.randint(0, self.H - lz)
+            new_anchor = (x_new, y_new, z_new)
+            old_anchor = inst.anchor
+
+            if new_anchor == old_anchor:
+                continue
+
+            # Validate: new footprint is void or self-owned
+            if not self._cells_clear(current_state, x_new, y_new, z_new, lx, ly, lz, allow_id=inst_id):
+                continue
+
+            old_cells = inst.cells_at(old_anchor)
+            new_cells = inst.cells_at(new_anchor)
+            changed_cells = set(old_cells) | set(new_cells)
+            affected_ids = self._instances_reading_cells(changed_cells)
+            affected_ids.add(inst_id)
+
+            # Score before
+            before_local = self._evaluate_local_penalty(current_state, changed_cells, affected_ids)
+
+            # Apply move
+            for (x, y, z) in old_cells:
+                current_state[x, y, z] = 0
+            for (x, y, z) in new_cells:
+                current_state[x, y, z] = inst_id
+            inst.anchor = new_anchor
+            self._update_work_cache_for_move(inst, old_anchor, new_anchor)
+
+            # Score after (note: instance.anchor is now new_anchor, so its blocking is
+            # evaluated at the new position; affected_ids set doesn't change because it
+            # already included this instance.)
+            after_local = self._evaluate_local_penalty(current_state, changed_cells, affected_ids)
+            delta_P = after_local - before_local
+            proposed_P = current_P + delta_P
+            proposed_W = self._work_from_cache()
+            delta_W = proposed_W - current_W
+            delta_H = delta_W + gamma * delta_P
+
+            if delta_H < 0 or random.random() < math.exp(-delta_H / max(T, 1e-12)):
+                current_P = proposed_P
+                current_W = proposed_W
+                current_H = current_W + gamma_max * current_P
+                if current_H < best_H:
+                    best_H = current_H
+                    best_state = np.copy(current_state)
+                if current_P == 0 and current_W < best_feasible_W:
+                    best_feasible_W = current_W
+                    best_feasible_state = np.copy(current_state)
+            else:
+                # Revert
+                for (x, y, z) in new_cells:
+                    current_state[x, y, z] = 0
+                for (x, y, z) in old_cells:
+                    current_state[x, y, z] = inst_id
+                inst.anchor = old_anchor
+                self._update_work_cache_for_move(inst, new_anchor, old_anchor)
+
+        result_state = best_feasible_state if best_feasible_state is not None else best_state
+        # Sync instance anchors to the returned state — best_state was np.copy'd at peak
+        # quality, but self._instances has been moving along with current_state since.
+        self._sync_instances_to_state(result_state)
+        return result_state, self.physical_penalty(result_state), self.spatial_work(result_state)
+
+    # ---------- result inspection ----------
+
+    def _sync_instances_to_state(self, state):
+        """Set each instance's anchor to its min-corner in `state`. Drops instances
+        whose cells aren't found (shouldn't happen for an internally produced state)."""
+        missing = []
+        for inst_id, inst in self._instances.items():
+            cells = np.argwhere(state == inst_id)
+            if len(cells) == 0:
+                missing.append(inst_id)
+                continue
+            mn = cells.min(axis=0)
+            inst.anchor = (int(mn[0]), int(mn[1]), int(mn[2]))
+        for inst_id in missing:
+            del self._instances[inst_id]
+
+    def get_layout(self, state=None):
+        """
+        Return the optimized placement as a list of dicts, one per instance:
+            {id, client, item_type, is_return, anchor, shape, cells}
+
+        With no argument, reads the current synced anchors (set by optimize()).
+        Pass a state array to derive positions directly from it instead.
+        """
+        layout = []
+        if state is None:
+            for inst in self._instances.values():
+                layout.append({
+                    "id": inst.id,
+                    "client": inst.client,
+                    "item_type": inst.item_type,
+                    "is_return": inst.is_return,
+                    "anchor": inst.anchor,
+                    "shape": inst.shape,
+                    "cells": inst.cells,
+                })
+        else:
+            for inst in self._instances.values():
+                cells = np.argwhere(state == inst.id)
+                if len(cells) == 0:
+                    continue
+                anchor = tuple(int(v) for v in cells.min(axis=0))
+                layout.append({
+                    "id": inst.id,
+                    "client": inst.client,
+                    "item_type": inst.item_type,
+                    "is_return": inst.is_return,
+                    "anchor": anchor,
+                    "shape": inst.shape,
+                    "cells": [tuple(int(v) for v in c) for c in cells],
+                })
+        return layout
+
+
+# --- EXECUTION ---
+if __name__ == "__main__":
+    optimizer = SmartTruckOptimizer3D(
+        length_bays=8, width_pallets=4, height_layers=3, route=[1, 2, 3],
+        item_shapes={
+            "keg":   (1, 1, 1),
+            "crate": (2, 1, 1),
+            "tower": (1, 1, 2),
+        },
+    )
+
+    # {client: {item_type: total_count}}
+    orders = {
+        1: {"keg": 12, "crate": 4},
+        2: {"keg": 18, "crate": 6},
+        3: {"keg": 15, "tower": 4},
+    }
+    # {client: {item_type: returns_count}}  — must be ≤ total per (client, item)
+    returns = {
+        1: {"keg": 8},
+        2: {"keg": 5},
+        3: {"keg": 10},
+    }
+
+    initial = optimizer.generate_initial_state(orders, returns)
+    print("Initial 3D Penalty:        ", optimizer.physical_penalty(initial))
+    print(f"Initial Work (normalized): {optimizer.spatial_work(initial):.4f}")
+
+    final_state, final_P, final_W = optimizer.optimize(initial, steps=30000)
+
+    print(f"\nOptimization Complete.")
+    print(f"Final Physical Violations: {final_P}")
+    print(f"Final Work Metric:         {final_W:.4f}")
+
+    if final_P == 0:
+        print("\nValid 3D Lattice Layout generated successfully.")
+    else:
+        print("\nOptimizer trapped in local minimum. Try increasing 'steps'.")
+
+    layout = optimizer.get_layout()
+    print(f"\nLayout: {len(layout)} items")
+    for item in sorted(layout, key=lambda i: (i["client"], i["anchor"])):
+        kind = "return" if item["is_return"] else "delivery"
+        print(f"  client={item['client']} {item['item_type']:<6} {kind:<8} "
+              f"anchor={item['anchor']} shape={item['shape']}")
+
+
+
+
+'''
+  Input format for generate_initial_state:
+  orders  = {client_id: {item_type: total_count}}
+  returns = {client_id: {item_type: returns_count}}
+'''

products.json

@@ -0,0 +1,123 @@
+{
+  "products": [
+    {
+      "id": "1_not_returnable_brand1",
+      "length_cells": 1,
+      "width_cells": 1,
+      "height_cells": 1,
+      "weight_kg": 10.0,
+      "is_returnable": false,
+      "brand": "brand1"
+    },
+    {
+      "id": "2_not_returnable_brand1",
+      "length_cells": 1,
+      "width_cells": 1,
+      "height_cells": 2,
+      "weight_kg": 13.0,
+      "is_returnable": false,
+      "brand": "brand1"
+    },
+    {
+      "id": "1_returnable_brand1",
+      "length_cells": 1,
+      "width_cells": 1,
+      "height_cells": 1,
+      "weight_kg": 15.0,
+      "is_returnable": true,
+      "brand": "brand1"
+    },
+    {
+      "id": "2_returnable_brand1",
+      "length_cells": 1,
+      "width_cells": 1,
+      "height_cells": 2,
+      "weight_kg": 18.0,
+      "is_returnable": true,
+      "brand": "brand1"
+    },
+
+
+
+
+    {
+      "id": "1_not_returnable_brand2",
+      "length_cells": 1,
+      "width_cells": 1,
+      "height_cells": 1,
+      "weight_kg": 10.0,
+      "is_returnable": false,
+      "brand": "brand2"
+
+    },
+    {
+      "id": "2_not_returnable_brand2",
+      "length_cells": 1,
+      "width_cells": 1,
+      "height_cells": 2,
+      "weight_kg": 13.0,
+      "is_returnable": false,
+      "brand": "brand2"
+    },
+    {
+      "id": "1_returnable_brand2",
+      "length_cells": 1,
+      "width_cells": 1,
+      "height_cells": 1,
+      "weight_kg": 15.0,
+      "is_returnable": true,
+      "brand": "brand2"
+    },
+    {
+      "id": "2_returnable_brand2",
+      "length_cells": 1,
+      "width_cells": 1,
+      "height_cells": 2,
+      "weight_kg": 18.0,
+      "is_returnable": true,
+      "brand": "brand2"
+    },
+
+
+
+
+
+
+    {
+      "id": "1_not_returnable_brand3",
+      "length_cells": 1,
+      "width_cells": 1,
+      "height_cells": 1,
+      "weight_kg": 10.0,
+      "is_returnable": false,
+      "brand": "brand3"
+    },
+    {
+      "id": "2_not_returnable_brand3",
+      "length_cells": 1,
+      "width_cells": 1,
+      "height_cells": 2,
+      "weight_kg": 13.0,
+      "is_returnable": false,
+      "brand": "brand3"
+    },
+    {
+      "id": "1_returnable_brand3",
+      "length_cells": 1,
+      "width_cells": 1,
+      "height_cells": 1,
+      "weight_kg": 15.0,
+      "is_returnable": true,
+      "brand": "brand3"
+    },
+    {
+      "id": "2_returnable_brand3",
+      "length_cells": 1,
+      "width_cells": 1,
+      "height_cells": 2,
+      "weight_kg": 18.0,
+      "is_returnable": true,
+      "brand": "brand3"
+    }
+  ]
+}

render.yaml

@@ -0,0 +1,17 @@
+services:
+  - type: web
+    name: damm-truck-api
+    runtime: python
+    region: frankfurt          # closest Render region to Barcelona
+    plan: standard             # 2 GB RAM. Starter (512 MB) risks OOM on graph load.
+    rootDir: backend           # FastAPI app lives in /backend in the repo
+    pythonVersion: "3.12"
+    buildCommand: pip install -r requirements.txt
+    startCommand: uvicorn app:app --host 0.0.0.0 --port $PORT --workers 1
+    healthCheckPath: /health
+    autoDeploy: true
+    envVars:
+      - key: MPLBACKEND
+        value: Agg
+      - key: PYTHONUNBUFFERED
+        value: "1"

requirements.txt

@@ -0,0 +1,15 @@
+osmnx>=2.0
+networkx>=3.0
+shapely>=2.0
+matplotlib>=3.7
+pydantic>=2.5
+numpy>=1.26
+scikit-learn>=1.2
+pyproj>=3.6
+fastapi>=0.115
+uvicorn[standard]>=0.30
+pyproj>=3.6
+ortools>=9.10
+firebase-admin>=6.5
+anthropic>=0.40.0
+httpx>=0.27.2

sample_request.json

@@ -0,0 +1,169 @@
+{
+  "request_id": "REQ-2026-05-10-001",
+  "date": "2026-05-10",
+
+  "depot": {
+    "id": "WH-GR",
+    "coords": { "lat": 41.5750, "lng": 2.2500 },
+    "open": "07:00",
+    "close": "20:00"
+  },
+
+  "fleet": {
+    "num_vans": 3,
+    "van_type": "8_pallets",
+    "vans_ref": "vans.json",
+    "products_ref": "products.json"
+  },
+
+  "drivers": [
+    { "id": "D01", "shift_start": "07:00", "shift_end": "15:00" },
+    { "id": "D02", "shift_start": "07:00", "shift_end": "15:00" },
+    { "id": "D03", "shift_start": "07:30", "shift_end": "15:30" }
+  ],
+
+  "stops": [
+    {
+      "id": "S001",
+      "coords": { "lat": 41.6052739, "lng": 2.2928775 },
+      "time_window": { "open": "08:00", "close": "12:00" },
+      "deliveries": [
+        { "product_id": "1_not_returnable_brand1", "qty": 24 },
+        { "product_id": "2_not_returnable_brand2", "qty": 12 },
+        { "product_id": "1_returnable_brand1",     "qty": 18 }
+      ],
+      "pickups": [
+        { "product_id": "1_returnable_brand1", "qty": 18 },
+        { "product_id": "2_returnable_brand2", "qty": 6 }
+      ]
+    },
+    {
+      "id": "S002",
+      "coords": { "lat": 41.5975345, "lng": 2.2884292 },
+      "time_window": { "open": "08:30", "close": "10:30" },
+      "deliveries": [
+        { "product_id": "2_not_returnable_brand1", "qty": 8 },
+        { "product_id": "2_returnable_brand2",     "qty": 16 }
+      ],
+      "pickups": [
+        { "product_id": "2_returnable_brand2", "qty": 16 }
+      ]
+    },
+    {
+      "id": "S003",
+      "coords": { "lat": 41.5428223, "lng": 2.2161158 },
+      "time_window": { "open": "09:00", "close": "13:00" },
+      "deliveries": [
+        { "product_id": "1_not_returnable_brand3", "qty": 30 },
+        { "product_id": "1_returnable_brand1",     "qty": 20 }
+      ],
+      "pickups": [
+        { "product_id": "1_returnable_brand1", "qty": 25 }
+      ]
+    },
+    {
+      "id": "S004",
+      "coords": { "lat": 41.5393484, "lng": 2.2130934 },
+      "time_window": { "open": "09:00", "close": "11:00" },
+      "deliveries": [
+        { "product_id": "2_not_returnable_brand2", "qty": 14 },
+        { "product_id": "2_returnable_brand3",     "qty": 10 }
+      ],
+      "pickups": [
+        { "product_id": "2_returnable_brand3", "qty": 8 }
+      ]
+    },
+    {
+      "id": "S005",
+      "coords": { "lat": 41.5479180, "lng": 2.2083603 },
+      "time_window": { "open": "10:00", "close": "14:00" },
+      "deliveries": [
+        { "product_id": "1_not_returnable_brand2", "qty": 18 },
+        { "product_id": "2_not_returnable_brand2", "qty": 6 }
+      ],
+      "pickups": []
+    },
+    {
+      "id": "S006",
+      "coords": { "lat": 41.5484516, "lng": 2.2067478 },
+      "time_window": { "open": "08:00", "close": "11:30" },
+      "deliveries": [
+        { "product_id": "1_returnable_brand3", "qty": 36 }
+      ],
+      "pickups": [
+        { "product_id": "1_returnable_brand3", "qty": 30 }
+      ]
+    },
+    {
+      "id": "S007",
+      "coords": { "lat": 41.5450720, "lng": 2.2056984 },
+      "time_window": { "open": "11:00", "close": "13:00" },
+      "deliveries": [
+        { "product_id": "1_not_returnable_brand1", "qty": 10 },
+        { "product_id": "2_returnable_brand1",     "qty": 14 }
+      ],
+      "pickups": [
+        { "product_id": "2_returnable_brand1", "qty": 12 }
+      ]
+    },
+    {
+      "id": "S008",
+      "coords": { "lat": 41.5381651, "lng": 2.2113491 },
+      "time_window": { "open": "08:30", "close": "12:30" },
+      "deliveries": [
+        { "product_id": "2_not_returnable_brand3", "qty": 20 },
+        { "product_id": "1_returnable_brand2",     "qty": 10 }
+      ],
+      "pickups": [
+        { "product_id": "1_returnable_brand2", "qty": 8 }
+      ]
+    },
+    {
+      "id": "S009",
+      "coords": { "lat": 41.5367766, "lng": 2.2135257 },
+      "time_window": { "open": "09:30", "close": "13:30" },
+      "deliveries": [
+        { "product_id": "1_not_returnable_brand2", "qty": 22 },
+        { "product_id": "2_not_returnable_brand1", "qty": 8 },
+        { "product_id": "2_returnable_brand2",     "qty": 12 }
+      ],
+      "pickups": [
+        { "product_id": "2_returnable_brand2", "qty": 10 }
+      ]
+    },
+    {
+      "id": "S010",
+      "coords": { "lat": 41.5443121, "lng": 2.2194830 },
+      "time_window": { "open": "10:30", "close": "14:30" },
+      "deliveries": [
+        { "product_id": "1_returnable_brand1", "qty": 14 },
+        { "product_id": "2_returnable_brand3", "qty": 8 }
+      ],
+      "pickups": [
+        { "product_id": "1_returnable_brand1", "qty": 12 },
+        { "product_id": "2_returnable_brand3", "qty": 6 }
+      ]
+    },
+    {
+      "id": "S011",
+      "coords": { "lat": 41.5377848, "lng": 2.2129105 },
+      "time_window": { "open": "08:00", "close": "10:00" },
+      "deliveries": [
+        { "product_id": "1_not_returnable_brand3", "qty": 16 }
+      ],
+      "pickups": []
+    },
+    {
+      "id": "S012",
+      "coords": { "lat": 41.5373051, "lng": 2.2120439 },
+      "time_window": { "open": "11:30", "close": "14:00" },
+      "deliveries": [
+        { "product_id": "2_not_returnable_brand1", "qty": 10 },
+        { "product_id": "1_returnable_brand2",     "qty": 24 }
+      ],
+      "pickups": [
+        { "product_id": "1_returnable_brand2", "qty": 20 }
+      ]
+    }
+  ]
+}

solver.py

@@ -0,0 +1,157 @@
+"""OR-tools VRPSPD-TW solver.
+
+One-shot solve: capacity (cells + kg), per-stop time windows, per-driver
+shifts, simultaneous pickup-delivery, single depot.
+
+Capacity model is the conservative VRPSPD bound:
+    sum(delivery_cells + pickup_cells on route) <= van_capacity_cells
+    sum(delivery_kg    + pickup_kg    on route) <= van_capacity_kg
+
+This bounds the *peak* in-truck load by the *sum* of inflow + outflow on the
+route. Slightly suboptimal vs. the exact load-profile model but correct
+(never accepts an infeasible plan) and fast.
+
+Objective: minimize total travel time across the fleet.
+Time window + shift constraints are HARD (set via SetRange).
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+from ortools.constraint_solver import pywrapcp, routing_enums_pb2
+
+from loader import Depot, Driver, Fleet, Stop
+from travel_time import TravelMatrix
+
+
+@dataclass
+class StopPlan:
+    sequence: int
+    id: str
+    arrival_s: int
+
+
+@dataclass
+class VanPlan:
+    van_idx: int
+    driver_id: str
+    stops: list[StopPlan]
+    travel_s: int
+    total_s: int          # depot-to-depot wall clock incl. waits + service
+    peak_cells: int
+    peak_kg: int
+    feasible: bool
+    violations: list[str] = field(default_factory=list)
+
+
+@dataclass
+class FleetPlan:
+    vans: list[VanPlan]
+    drive_s: int
+    total_s: int
+    all_feasible: bool
+
+
+def solve_vrp(
+    depot: Depot,
+    fleet: Fleet,
+    drivers: list[Driver],
+    stops: list[Stop],
+    matrix: TravelMatrix,
+    time_limit_s: int = 5,
+) -> FleetPlan:
+    """Index 0 = depot, 1..n = stops in input order."""
+    n = len(stops) + 1
+    K = fleet.num_vans
+    H = 24 * 3600
+
+    manager = pywrapcp.RoutingIndexManager(n, K, 0)
+    routing = pywrapcp.RoutingModel(manager)
+
+    # Arc cost = pure travel time (objective).
+    def arc_cb(fi, ti):
+        i, j = manager.IndexToNode(fi), manager.IndexToNode(ti)
+        return int(matrix.time_s[i, j])
+    routing.SetArcCostEvaluatorOfAllVehicles(routing.RegisterTransitCallback(arc_cb))
+
+    # Time dimension = travel + service at "from" node.
+    def time_cb(fi, ti):
+        i, j = manager.IndexToNode(fi), manager.IndexToNode(ti)
+        s = stops[i - 1].service_time_s if i >= 1 else 0
+        return int(matrix.time_s[i, j] + s)
+    time_idx = routing.RegisterTransitCallback(time_cb)
+    routing.AddDimension(time_idx, H, H, False, "Time")
+    time_dim = routing.GetDimensionOrDie("Time")
+
+    for vid, d in enumerate(drivers):
+        time_dim.CumulVar(routing.Start(vid)).SetRange(d.shift_start_s, d.shift_end_s)
+        time_dim.CumulVar(routing.End(vid)).SetRange(d.shift_start_s, d.shift_end_s)
+    for i, s in enumerate(stops):
+        time_dim.CumulVar(manager.NodeToIndex(i + 1)).SetRange(s.t_open_s, s.t_close_s)
+
+    # Capacity: cells.
+    def cells_cb(fi):
+        i = manager.IndexToNode(fi)
+        return 0 if i == 0 else stops[i - 1].delivery_cells + stops[i - 1].pickup_cells
+    routing.AddDimensionWithVehicleCapacity(
+        routing.RegisterUnaryTransitCallback(cells_cb),
+        0, [fleet.capacity_cells] * K, True, "Cells",
+    )
+
+    # Capacity: kg.
+    def kg_cb(fi):
+        i = manager.IndexToNode(fi)
+        return 0 if i == 0 else int(round(stops[i - 1].delivery_kg + stops[i - 1].pickup_kg))
+    routing.AddDimensionWithVehicleCapacity(
+        routing.RegisterUnaryTransitCallback(kg_cb),
+        0, [int(fleet.capacity_kg)] * K, True, "Kg",
+    )
+
+    sp = pywrapcp.DefaultRoutingSearchParameters()
+    sp.first_solution_strategy = routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
+    sp.local_search_metaheuristic = routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH
+    sp.time_limit.seconds = int(time_limit_s)
+
+    sol = routing.SolveWithParameters(sp)
+    if sol is None:
+        return FleetPlan([], 0, 0, False)
+
+    vans: list[VanPlan] = []
+    fleet_drive = fleet_total = 0
+    all_feas = True
+    for vid in range(K):
+        idx = routing.Start(vid)
+        plan: list[StopPlan] = []
+        seq = peak_c = peak_k = cum_c = cum_k = v_drive = 0
+        while not routing.IsEnd(idx):
+            node = manager.IndexToNode(idx)
+            if node != 0:
+                seq += 1
+                arr = sol.Value(time_dim.CumulVar(idx))
+                s = stops[node - 1]
+                cum_c += s.delivery_cells + s.pickup_cells
+                cum_k += int(round(s.delivery_kg + s.pickup_kg))
+                peak_c = max(peak_c, cum_c)
+                peak_k = max(peak_k, cum_k)
+                plan.append(StopPlan(seq, s.id, int(arr)))
+            nxt = sol.Value(routing.NextVar(idx))
+            v_drive += int(matrix.time_s[manager.IndexToNode(idx), manager.IndexToNode(nxt)])
+            idx = nxt
+        end_t = sol.Value(time_dim.CumulVar(routing.End(vid)))
+        v_total = end_t - drivers[vid].shift_start_s
+        feas = v_total <= drivers[vid].shift_end_s - drivers[vid].shift_start_s
+        vans.append(VanPlan(
+            van_idx=vid,
+            driver_id=drivers[vid].id,
+            stops=plan,
+            travel_s=v_drive,
+            total_s=v_total,
+            peak_cells=peak_c,
+            peak_kg=peak_k,
+            feasible=feas,
+        ))
+        fleet_drive += v_drive
+        fleet_total += v_total
+        all_feas &= feas
+    return FleetPlan(vans, fleet_drive, fleet_total, all_feas)

travel_time.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:361d146d55bef6afbbb6275213cd05167636f6248ddfc7c4e44a94851f64c250
+size 752764

travel_time.py

@@ -0,0 +1,77 @@
+"""All-pairs travel-time + distance matrix between snapped OSM road nodes.
+
+Snaps each (lat, lng) to its nearest road node, then runs one Dijkstra per
+unique source on the *directed* graph (one-ways respected). The matrix is
+asymmetric and dense — every fitness evaluation downstream is now an O(1)
+numpy lookup.
+"""
+
+from __future__ import annotations
+
+import csv
+import json
+import time
+from dataclasses import dataclass
+from pathlib import Path
+from dataclasses import dataclass
+
+import networkx as nx
+import numpy as np
+import osmnx as ox
+import pyproj
+
+from graph_manager import DEFAULT_COORDS_PATH, get_or_build_graph
+
+DEFAULT_MATRIX_PATH = Path(__file__).with_name("travel_time.npz")
+
+@dataclass
+class TravelMatrix:
+    point_ids: list[str]
+    node_ids: list[int]
+    time_s: np.ndarray
+    dist_m: np.ndarray
+
+    def index_of(self, point_id: str) -> int:
+        return self.point_ids.index(point_id)
+
+
+def _snap(graph: nx.MultiDiGraph, points: list[tuple[float, float]]) -> list[int]:
+    proj = ox.projection.project_graph(graph)
+    transformer = pyproj.Transformer.from_crs(
+        graph.graph["crs"], proj.graph["crs"], always_xy=True
+    )
+    lats = [p[0] for p in points]
+    lngs = [p[1] for p in points]
+    proj_x, proj_y = transformer.transform(lngs, lats)
+    return list(ox.distance.nearest_nodes(proj, X=list(proj_x), Y=list(proj_y)))
+
+
+def build_matrix(
+    graph: nx.MultiDiGraph,
+    points: list[tuple[str, float, float]],
+) -> TravelMatrix:
+    point_ids = [p[0] for p in points]
+    node_ids = _snap(graph, [(p[1], p[2]) for p in points])
+
+    n = len(points)
+    time_s = np.full((n, n), np.inf, dtype=np.float32)
+    dist_m = np.full((n, n), np.inf, dtype=np.float32)
+
+    by_node: dict[int, list[int]] = {}
+    for i, nd in enumerate(node_ids):
+        by_node.setdefault(nd, []).append(i)
+    targets = set(node_ids)
+
+    for src_node, src_rows in by_node.items():
+        t_to = nx.single_source_dijkstra_path_length(graph, src_node, weight="travel_time")
+        d_to = nx.single_source_dijkstra_path_length(graph, src_node, weight="length")
+        for tgt in targets:
+            t = t_to.get(tgt, np.inf)
+            d = d_to.get(tgt, np.inf)
+            for j in by_node[tgt]:
+                for i in src_rows:
+                    time_s[i, j] = t
+                    dist_m[i, j] = d
+    np.fill_diagonal(time_s, 0.0)
+    np.fill_diagonal(dist_m, 0.0)
+    return TravelMatrix(point_ids, node_ids, time_s, dist_m)

travel_time_request.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4b79abedec4c14ebad35c31033265bbdefe5b6667a59e43fac901d2a5ebf4c2
+size 2318

vans.json

@@ -0,0 +1,37 @@
+{
+  "_reference": "Mitsubishi Fuso Canter 515 Wide Cab Pantech (8_pallets); 6_pallets is a shorter chassis variant.",
+  "_units": {
+    "length_m": "interior length",
+    "width_m": "interior width",
+    "height_m": "interior height",
+    "cube_size_m": "discretization step for the lattice loader",
+    "pallet_length_m": "EUR pallet long side",
+    "pallet_width_m": "EUR pallet short side",
+    "pallet_height_m": "empty pallet board thickness"
+  },
+  "van_types": [
+    {
+      "type": "8_pallets",
+      "length_m": 4.8,
+      "width_m": 2.4,
+      "height_m": 2.1,
+      "cube_size_m": 0.4,
+      "pallet_length_m": 1.2,
+      "pallet_width_m": 1.2,
+      "pallet_height_m": 0.15,
+      "max_payload_kg": 3000
+    },
+    {
+      "type": "6_pallets",
+      "length_m": 3.6,
+      "width_m": 2.4,
+      "height_m": 2.1,
+      "cube_size_m": 0.4,
+      "pallet_length_m": 1.2,
+      "pallet_width_m": 1.2,
+      "pallet_height_m": 0.15,
+      "max_payload_kg": 2200
+    }
+  ],
+  "comments": "cube size = 0.4 / 5, Input format for generate_initial_state:  orders  = {client_id: {item_type: total_count}},  returns = {client_id: {item_type: returns_count}}"
+}