A Full-Stack C++ & Python Engine for National Energy Infrastructure Optimization
GATP is a high-performance routing and failover system designed to solve the multi-billion-dollar Right of Way (RoW) bottleneck in the renewable energy sector. By shifting from standard "shortest physical path" algorithms to dynamic, sociologically-weighted heuristics, GATP mathematically minimizes farmer protests, legal injunctions, and stranded energy costs during high-tension transmission line construction.
Renewable mega-parks are generating power faster than the grid can absorb it. Building 765kV transmission corridors requires planting massive steel towers on agricultural land, leading to:
- Severe Sociological Friction: Multi-crop farmers aggressively protest land acquisition.
- Legal Paralysis: Court injunctions halt construction for 1.5 to 3 years.
- Stranded Energy: Millions of dollars of generated solar/wind power are wasted daily.
Instead of routing power lines using direct physical distance, GATP utilizes a custom A Search Algorithm* backed by a Sociological Heuristic Engine. The engine calculates the true "Composite Weight" of a route by evaluating physical engineering costs, state jurisdiction taxes, and farmer protest probability models.
- Pre-Emptive Routing: Uses A Search* to find the most cost-effective, lowest-risk infrastructure paths, automatically bypassing high-protest farming zones.
- Max-Flow Failover Engine: Uses the Ford-Fulkerson Algorithm (BFS) to simulate catastrophic transmission line failures (e.g., towers collapsing) and instantly calculates how to safely reroute megawatts of live power through backup grid corridors.
- The system is completely decoupled from hardcoded logic. It features a custom C++ File Parser that reads dynamic
nodes.csv(geographic/risk data) andedges.csv(infrastructure capacity) files, allowing for massive scalability without recompiling the core engine.
- A Python
NetworkXandMatplotliblayer that parses the C++ data outputs and renders a mathematically accurate, color-coded topological map of the infrastructure network.
(Green = Standard Clearance | Red = Critical Sociological Friction)
The C++ engine runs an interactive CLI allowing the user to select starting and destination infrastructure nodes via text parsing. It automatically contrasts the standard industry route against the algorithmically optimized route.
[BASELINE]: Traditional Direct Route
- Baseline Cost Estimate: ₹15.51 Billion
- Risk Profile: CRITICAL (Severe multi-crop farmer protests expected)
[ALGORITHM]: Optimized Route Discovered
- Optimized Project Cost: ₹10.78 Billion
- Financial Savings: ₹4.73 Billion
- Routing Sequence: Khavda -> Bhuj -> Surendranagar -> Ahmedabad
- Backend: C++14/C++17 (Adjacency Lists, Min-Heaps, Pointers, File I/O)
- Algorithms: A* Search, Dijkstra's Variation, Ford-Fulkerson (Max-Flow)
- Visualization: Python 3 (Pandas, NetworkX, Matplotlib)
To compile and run locally:
g++ main.cpp -o gatp_core
./gatp_core