An application of ant colony optimization in industrial training allocation

Ramona Ramli, Navhin Gopal

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)61-64
Number of pages4
JournalJournal of Telecommunication, Electronic and Computer Engineering
Volume9
Issue number2-2
Publication statusPublished - 01 Jan 2017

Fingerprint

Ant colony optimization
Supervisory personnel
Students
Costs

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Cite this

@article{09ef9cb6869f49cfb6669c490b399850,
title = "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.",
author = "Ramona Ramli and Navhin Gopal",
year = "2017",
month = "1",
day = "1",
language = "English",
volume = "9",
pages = "61--64",
journal = "Journal of Telecommunication, Electronic and Computer Engineering",
issn = "2180-1843",
publisher = "Universiti Teknikal Malaysia Melaka",
number = "2-2",

}

An application of ant colony optimization in industrial training allocation. / Ramli, Ramona; Gopal, Navhin.

In: Journal of Telecommunication, Electronic and Computer Engineering, Vol. 9, No. 2-2, 01.01.2017, p. 61-64.

Research output: Contribution to journalArticle

TY - JOUR

T1 - An application of ant colony optimization in industrial training allocation

AU - Ramli, Ramona

AU - Gopal, Navhin

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85032914200&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85032914200&partnerID=8YFLogxK

M3 - Article

VL - 9

SP - 61

EP - 64

JO - Journal of Telecommunication, Electronic and Computer Engineering

JF - Journal of Telecommunication, Electronic and Computer Engineering

SN - 2180-1843

IS - 2-2

ER -