Genetic Algorithm and Particle Swarm Optimization Techniques for Solving Multi-Objectives on Single Machine Scheduling Problem

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Alaa Sabah Hameed Hanan Ali Chachan

Abstract

In this paper, two of the local search algorithms are used (genetic algorithm and particle swarm optimization), in scheduling number of products (n jobs) on a single machine to minimize a multi-objective function which is denoted as  (total completion time, total tardiness, total earliness and the total late work). A branch and bound (BAB) method is used for comparing the results for (n) jobs starting from (5-18). The results show that the two algorithms have found the optimal and near optimal solutions in an appropriate times.

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How to Cite
HAMEED, Alaa Sabah; CHACHAN, Hanan Ali. Genetic Algorithm and Particle Swarm Optimization Techniques for Solving Multi-Objectives on Single Machine Scheduling Problem. Ibn AL- Haitham Journal For Pure and Applied Science, [S.l.], v. 33, n. 1, p. 119-128, jan. 2020. ISSN 2521-3407. Available at: <http://jih.uobaghdad.edu.iq/index.php/j/article/view/2378>. Date accessed: 25 may 2020. doi: http://dx.doi.org/10.30526/33.1.2378.
Section
mathematics