Optimizing Exam Hall Allocation Using a Radio Frequency Identification-Based Smart Admit Card System and Genetic Algorithm

Abstract

Many examination centers still manage seat allocation and student verification manually, which often leads to delays, inconsistent records, and limited real-time control during exams. This study developed and evaluated an integrated system that manages both seat allocation and student verification within a single workflow. The system employs an RFID-based smart admit card mechanism together with a genetic-algorithm-inspired seat allocation method built using Django, Next.js, and Raspberry Pi hardware. The allocation process reduces same-college adjacency, avoids the unnecessary use of extra rooms, and minimizes poor seat spacing, while RFID scanning confirms each student’s identity and assigned seat upon entry. Validation was conducted using 34 formal test cases (20 unit tests and 14 system tests) covering model behavior, API endpoints, algorithm constraints, authentication, data management, and end-to-end workflows; all tests passed within the implemented scope. The allocation process handled a 50-student classroom in an average of 2.3 seconds, and RFID verification averaged 0.8 seconds per student. These results demonstrate that the system is practical for the tested setting and can support more consistent, fair, and manageable examination operations.