Sixty Second Santa Run

Setting Up a Fast Santa-Route Optimizer

Sixty Second Santa Run is what I call the practice of getting a working gift-delivery routing solution off the ground in under a minute. You don't need fancy software. You need a starting point, a list of stops, and an approach that skips the heavy math. Most people overcomplicate it on day one. They go straight for integer programming solvers or genetic algorithms when what they actually need is something that returns a decent path before lunch. Here is the method I use. First, collect your delivery points into a single CSV or JSON file with latitude and longitude columns. Call it addresses.csv. Second, decide on your vehicle capacity, time windows, and whether returns to depot are required. Third, run a nearest-neighbor heuristic as your baseline. That gets you a route in seconds. Fourth, if the result looks wrong, apply a quick 2-opt local search to clean up crossings. That usually happens in well under sixty seconds on any modern laptop. I keep a small Python script for this. It uses scipy.spatial.distance.cdist or a simple haversine function for distance calculation. The file is tiny. You clone it, drop in your addresses, and run. No database setup. No cloud instance. Just input, output, and a GPX or GeoJSON file you can view in a browser or QGIS. For a typical residential route with fifty stops, this takes about eight to twelve seconds on my machine and produces a route that is usually within ten to eighteen percent of the known optimal, depending on how clustered the points are.

What beginners miss about the nearest-neighbor start

The nearest-neighbor heuristic is not elegant, but it is stubbornly useful. The mistake most people make is starting from an arbitrary point. That choice matters a lot. If you begin from the depot and sort by polar angle, you get one shape. If you begin from the geographically easternmost stop and sweep clockwise, you often get fewer long backtrack legs. Try both and take the shorter total distance. It costs nothing extra and it fixes a lot of ugly routing artifacts early. Another thing nobody tells you about time windows: hard time windows turn a quick heuristic job into something that breaks fast. If your stops have strict delivery windows, nearest-neighbor alone will violate them almost immediately. In that case, add a simple feasibility filter that rejects any next-stop candidate whose arrival time falls outside the window. It slows things down slightly, but the route stays usable. If your windows are soft instead of hard, penalize violations in the cost function rather than banning them outright. That keeps the solver from grinding to a halt.

A real edge case I ran into

Last winter I was routing a holiday campaign for a regional distributor with about two hundred stops across three counties. The nearest-neighbor pass produced a route that looked fine on paper but had one terrible leg: a single jump of fourteen kilometers between two clusters because the heuristic locked onto a midpoint address that should have been split into a different vehicle. The fix was simple. I introduced a quick clustering prepass using K-means with K equal to the number of available vehicles, then ran nearest-neighbor inside each cluster before stitching the cluster centroids together with a greedy insert. That one change cut total distance by roughly twenty-three percent and eliminated the outlier leg. The whole thing still completed in under a minute. Do not assume haversine distances are enough when your actual routing involves highways and one-way streets. Haversine works fine for dense urban neighborhoods where block distances are predictable. It falls apart quickly in suburban or rural areas where the road network adds significant overhead. If your area has a mix, use a real routing API like OSRM or Valhalla for the final pass, even if you only run it once. The difference between Euclidean and road-distance can be thirty to sixty percent on total route length in some regions. Another common trap is ignoring return-to-depot cost. A route that ends three towns away looks shorter in total stop-to-stop distance, but once you add the return leg, it may be worse than a route that circles back efficiently. Always include the return distance in your objective unless you have a specific reason not to.

When this approach stops working

Sixty Second Santa Run is not a universal solution. It breaks down when you have hundreds of stops with complex time windows, multiple depots, heterogeneous fleet capacities, or stochastic travel times. In those cases, the quick heuristic will give you something fast but unreliable, and you are better off moving to a proper constraint programming solver or a commercial route optimizer. The sixty-second workflow is a starting line, not the finish line. Use it to validate data, sanity-check outputs, and prototype before committing to a heavier toolchain. Get your address list cleaned up first. Remove duplicates, standardize formatting, and geocode anything missing coordinates. Validate that your coordinate system is WGS84. Run the nearest-neighbor pass. Check the output visually. Apply 2-opt if crossings are obvious. Re-run with a different starting point and compare. Export to GPX and load it into your routing software or dashboard. That is the full loop. Most of the work is in the data prep, not the algorithm. If you want a minimal reference implementation, I keep a small repo with the CSV parser, the nearest-neighbor builder, the 2-opt cleaner, and the GPX exporter all in one script. It is not production grade. It is meant to get you a usable route in about a minute so you can stop guessing and start iterating on the actual problem.