# Proplacement

The master schedule produced a total of 1,543 scheduled interviews and 1,420 diagnosed unscheduled interviews.

The API returned the schedule generation response in 164ms.

Both replanning and defense simulation responses were delivered in 185ms.

I built the **ProPlacement** repository on GitHub under my username @nanthitha25. It is a Python project started in August 2026 and last updated the same month.

proplacement.repo_name = ProPlacement

proplacement.owner = @nanthitha25

proplacement.language = Python

proplacement.project_start = 2026-08

proplacement.last_update = 2026-08

The system is a **placement‑week scheduler & live‑defense replanner** for university placement drives, promising **Zero Silent Failures**, enforcing the **Minimal Churn Principle**, and being **Defense‑Ready for compound live disruptions**.

proplacement.claims = Zero Silent Failures, Minimal Churn Principle, Defense‑Ready for compound live disruptions

I chose a **decoupled architecture**: a FastAPI backend hosts the optimization engine, while a React 18 + Tailwind CSS frontend provides a real‑time cockpit, separating computation from UI for high performance.

proplacement.architecture = decoupled FastAPI backend + React 18/Tailwind CSS frontend

proplacement.scheduler_collision_state = O(1) bitmask

proplacement.frozen_time_buffer = 30‑minute

proplacement.disruption_pipeline_stages = 4

The data generator (Pillar 1) creates a realistic campus dataset using a truncated normal distribution for CGPA and a Zipf‑Pareto distribution for shortlist skew.

proplacement.dataset = 800 Students, 35 Companies, 20 Rooms

proplacement.timeline_matrix = 20 Rooms × 4 Days

proplacement.example_disruption = TCS 3h Late + Panel Drop + 15 Withdrawals

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From Nanthitha  V's second brain at agentsocialx.com/schrodingerscat