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Using Genetic Algorithm to Generate Pareto-Front in Multi-Objective Problem

by Ir. M. Dachyar, Farizal, Celine Kurniajaya
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 51
Year of Publication: 2018
Authors: Ir. M. Dachyar, Farizal, Celine Kurniajaya
10.5120/ijca2018917346

Ir. M. Dachyar, Farizal, Celine Kurniajaya . Using Genetic Algorithm to Generate Pareto-Front in Multi-Objective Problem. International Journal of Computer Applications. 180, 51 ( Jun 2018), 21-25. DOI=10.5120/ijca2018917346

@article{ 10.5120/ijca2018917346,
author = { Ir. M. Dachyar, Farizal, Celine Kurniajaya },
title = { Using Genetic Algorithm to Generate Pareto-Front in Multi-Objective Problem },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2018 },
volume = { 180 },
number = { 51 },
month = { Jun },
year = { 2018 },
issn = { 0975-8887 },
pages = { 21-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number51/29586-2018917346/ },
doi = { 10.5120/ijca2018917346 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:04:13.589258+05:30
%A Ir. M. Dachyar
%A Farizal
%A Celine Kurniajaya
%T Using Genetic Algorithm to Generate Pareto-Front in Multi-Objective Problem
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 51
%P 21-25
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Business must minimized time and cost for their operational activity. By doing this, customer can increase their satisfaction. In order to fulfill every order, one of core activity was material order process. Material order process optimization was multi-objective problem because there is a need to minimize both cost and time used for this process. Genetic Algorithm was used to solve this problem. This paper aims to provide pareto-front solutions for material order process multi-objective problem.

References
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  3. D.A. Coley. 1999 An Introduction to Genetic Algorithms for Scientists and Engineers. New Jersey.
  4. E. Zitzler, K. Deb, L. Thiele. 2000. Comparison of multi-objective evolutionary algorithms: empirical results. Evolutionary Computation 8 (2000), 125–148.
  5. ww2.mathworks.cn (Accessed on May 20th, 2018 at 1 pm)
  6. Engelbrecht A.P. 2002. Computational Intelligence: an Introduction. Wiley, New York.
  7. J.H. Holland. 1975. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, MI
  8. Mitchell. M. 1998. An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA
Index Terms

Computer Science
Information Sciences

Keywords

Pareto-front genetic algorithm multi-objective problem objective function constrained optimization.

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