Ivan-Volokhov

Ivan-Volokhov / Solving-task-for-Data-Fusion.-Heroes

Public

Здесь я показываю как удалось решить задачу на оптимизацию пути мультиагентной задачи.

11
0
100% credibility
Found Mar 30, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
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AI Summary

A solver for a puzzle where heroes from a castle visit time-limited mills over seven days to maximize timely captures and score.

How It Works

1
🏰 Discover the Heroes Puzzle Solver

You hear about a fun challenge where heroes race from a castle to capture mills that open on specific days over a week.

2
📁 Gather Your Game Data

You collect the lists of heroes, mills with opening days, and travel distances between them.

3
▶️ Launch the Solver

You start the tool, and it begins crunching numbers to find the smartest paths for your heroes.

4
⚡ Watch Routes Optimize

The solver smartly assigns mills to heroes, balancing travel time, waiting, and daily limits to hit as many on time as possible for the highest score.

5
📊 Explore the Animated Map

You open a vibrant interactive visualization that animates heroes' journeys day by day, showing timely captures in green and misses in red.

6
📄 Get Your Solution File

You receive a simple list pairing each hero with their assigned mills, ready to submit for the challenge.

🎉 Achieve Top Score

Your optimized plan maximizes rewards minus hero costs, letting you compete at the top of the leaderboard.

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AI-Generated Review

What is Solving-task-for-Data-Fusion.-Heroes?

This Python project solves a multi-agent optimization task where heroes must visit 700 mills opening across 7 days, maximizing timely captures (500 points each) while penalizing hero hires and travel. You feed it distance matrices, hero move points, and mill open days from CSV files, and it spits out an optimal route assignment in solution_heroes.csv plus an interactive HTML visualization tracking hero paths day-by-day with Plotly animations. It's built for data fusion challenges like Heroes, turning raw distances into scored routes.

Why is it gaining traction?

The beam search with suffix-aware prefixes and iterative subset rebuilds nails tight day-1 and day-3 optimizations better than naive greedy routers, often hitting high scores on 20-hero setups. Developers dig the zero-config run that auto-generates a playable HTML sim showing timely visits, hero progress, and stats—perfect for debugging routes visually without extra tools. At 11 stars, it's niche but hooks optimization hobbyists chasing leaderboard wins.

Who should use this?

Optimization engineers solving vehicle routing or TSP variants with time windows, like logistics planners assigning delivery fleets to timed depots. Competition coders in Kaggle-style data fusion tasks needing a strong baseline for multi-hero pathing. Researchers prototyping agent coordination who want quick viz of greedy-vs-beam tradeoffs.

Verdict

Grab it as a solid, runnable template for similar routing puzzles—tweaks to params like beam width yield quick gains—but the 1.0% credibility score and 11 stars signal low maturity, sparse docs, and hardcoded data paths needing adaptation. Worth forking for your next opti-challenge.

(198 words)

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