OptiSeat 🏛️

An algorithmic optimization engine for complex institutional seating arrangements.

OptiSeat is a high-performance Python application designed to solve the "combinatorial bottleneck" of exam and classroom seating. It replaces manual, error-prone processes with a deterministic allocation algorithm that ensures optimal resource utilization while maintaining strict academic integrity constraints.

🧠 The Algorithm: First Principles Allocation

OptiSeat doesn't just "place students in rooms." It employs a multi-pass allocation logic that handles:

• Relational Constraints: Links students, subjects, sections, and room capacities via a structured SQLite schema.
• Dynamic Capacity Management: Handles variable bench counts and student-per-bench densities.
• Sequential Integrity: Automatically detects and optimizes for sequential roll numbers to simplify post-allocation logistics.
• Multi-Date Orchestration: Processes multiple exam dates and subject pairs in a single execution loop.

✨ Key Features

• Automated Seat Allocation: Greedy-based optimization for filling rooms based on subject and section priority.
• Relational Data Engine: Powered by SQLite for robust, local-first data persistence.
• Post-Processing Intelligence: Converts raw allocation data into "Instruction Sets" (Range-based or Array-based) for easier implementation by invigilators.
• Streamlit GUI: A modern, reactive interface for data input and visualization.
• Sequence Detection: Identifies continuous roll number blocks (isSeq logic) to reduce the complexity of seating charts.

🛠️ Tech Stack

• Language: Python 3.10+
• Frontend: Streamlit
• Database: SQLite3
• Data Analysis: Pandas
• Serialization: Pickle for fast binary state recovery.

🚀 Getting Started

Installation

1. Clone the repository:

   git clone https://github.com/XotEmBotZ/OptiSeat.git
   cd OptiSeat

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   python main.py

📊 Logic Overview (allocateSeat)

The core engine iterates through distinct dates and subject pairings. It maintains a stateful studLst and fills rooms sequentially while validating against numBench and benchStud constraints. If a room cannot accommodate the required density, the algorithm raises an AssertionError, ensuring no "overflow" or illegal seating occurs.

💡 Use Case

Perfect for schools, colleges, and competitive exam centers that need to generate high-integrity seating plans for 1000+ students across multiple rooms and subjects in seconds.