An Application of Ant Colony Optimization in Industrial Training Allocation

Abstract

The process of assigning a visiting university’s supervisor to visit a group of industrial training practical students in the university is currently being done manually. In order to perform such task, two constraints need to be fulfilled at any time: (1) Practical student can only be supervised by university supervisor from the same department; (2) location of the places to be visited by the visiting university’s supervisor must be as near as possible in order to optimize the travelling cost, time and budget. Using manual approach, the process can be very tedious and time consuming especially when it involved large number of practical students and lecturers. Furthermore, the optimized result is seldom achievable as not all practical student-lecturer combinations are examined. By automating the process, the tedious and time consuming process can be avoided as well as establishing optimized combinations based on the given constraints. This paper discusses on how the assignment process is automated using Ant Colony Optimization (ACO). The results are then compared with Dijkstra’s Algorithm to evaluate the ability of ACO algorithms. The algorithm design, implementation, its future direction and improvements are discussed as well.