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Growing Science » International Journal of Industrial Engineering Computations » An imperialist competitive algorithm for a bi-objective parallel machine scheduling problem with load balancing consideration

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International Journal of Industrial Engineering Computations

ISSN 1923-2934 (Online) - ISSN 1923-2926 (Print)
Quarterly Publication
Volume 4 Issue 2 pp. 191-202 , 2013

An imperialist competitive algorithm for a bi-objective parallel machine scheduling problem with load balancing consideration Pages 191-202 Right click to download the paper Download PDF

Authors: Mansooreh Madani-Isfahani, Ehsan Ghobadian, Hassan Irani Tekmehdash, Reza Tavakkoli-Moghaddam, Mahdi Naderi-Beni

DOI: 10.5267/j.ijiec.2013.02.002

Keywords: Genetic algorithm, Imperialist competitive algorithm, Load Balancing, Parallel machine scheduling, Particle swarm optimization

Abstract: In this paper, we present a new Imperialist Competitive Algorithm (ICA) to solve a bi-objective unrelated parallel machine scheduling problem where setup times are sequence dependent. The objectives include mean completion time of jobs and mean squares of deviations from machines workload from their averages. The performance of the proposed ICA (PICA) method is examined using some randomly generated data and they are compared with three alternative methods including particle swarm optimization (PSO), original version of imperialist competitive algorithm (OICA) and genetic algorithm (GA) in terms of the objective function values. The preliminary results indicate that the proposed study outperforms other alternative methods. In addition, while OICA performs the worst as alternative solution strategy, PSO and GA seem to perform better.

How to cite this paper
Madani-Isfahani, M., Ghobadian, E., Tekmehdash, H., Tavakkoli-Moghaddam, R & Naderi-Beni, M. (2013). An imperialist competitive algorithm for a bi-objective parallel machine scheduling problem with load balancing consideration.International Journal of Industrial Engineering Computations , 4(2), 191-202.

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Journal: International Journal of Industrial Engineering Computations | Year: 2013 | Volume: 4 | Issue: 2 | Views: 3824 | Reviews: 0

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