Genetic Algorithm and Particle Swarm Optimization Techniques for Solving Multi-Objectives on Single Machine Scheduling Problem
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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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[1]
Hameed, A.S. and Chachan, H.A. 2020. Genetic Algorithm and Particle Swarm Optimization Techniques for Solving Multi-Objectives on Single Machine Scheduling Problem. Ibn AL-Haitham Journal For Pure and Applied Sciences. 33, 1 (Jan. 2020), 119–128. DOI:https://doi.org/10.30526/33.1.2378.
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Mathematics
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How to Cite
[1]
Hameed, A.S. and Chachan, H.A. 2020. Genetic Algorithm and Particle Swarm Optimization Techniques for Solving Multi-Objectives on Single Machine Scheduling Problem. Ibn AL-Haitham Journal For Pure and Applied Sciences. 33, 1 (Jan. 2020), 119–128. DOI:https://doi.org/10.30526/33.1.2378.