E-internship system with genetic algorithm

• Published in: 2017 IEEE Conference on e-Learning, e-Management and e-Services (IC3e) pp. 44 - 48
• Main Authors: Norizan, Nurhidayatul Ain Mohd, Taib, Shakirah Mohd, Zakaria, Nordin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-11-2017
• Subjects: automated assignment; Biological cells; Companies; Conferences; Genetic Algorithm; Genetic algorithms; internship coordination; Learning Management System; Resource management; Sociology; Statistics
• DOI: 10.1109/IC3e.2017.8409236

Internship program provides a hands-on opportunity for university students to gain experience in specific industries. Unstructured method in coordinating internship students lead to poor cost and manpower optimization, and poor compatibility between students and the assigned supervisors. The idea of augmenting an E-Internship system with Genetic Algorithm (GA) is to accommodate the need for a systematic and centralized system for internship coordination. E-Internship system is implemented as a Moodle function. The GA complements the system by automatically assigning a supervisor for each internship student based on two important criteria: distance between student location and number of students assigned to a lecturer or supervisor. An illustrative example of using GA for the Supervisor-Student Assignment (SSA) is provided. For evaluating the performance of the GA, an actual case was solved and the results show that the proposed GA is able to find nearly optimal solutions.