Implementasi Metode Hybrid Particle Swarm Optimization dan Genetic Algorithm Pada Penjadwalan Job Shop Scheduling

  • Anak Agung Putra Adnyana Student
  • I Made Widiartha
  • Agus Muliantara
  • Luh Gede Astuti
  • Made Agung Raharja
  • I Dewa Made Bayu Atmaja Darmawan

Abstract

Job shop problem is one of the non-deterministic combinatorial optimization problems with polynomial time (NP-complete). Genetic Algorithm optimization will be applied to solve Job Shop problems. hybrid particle swarm optimization. In this study.This Study is an attempt to solve Job Shop Scheduling problem using hybrid particle swarm optimization and genetic algorithm method, to find minimum Makespan. 5 parameters, C1, C2, inertia weight, crossover rate and mutation rate, will be compared with a range from 0.1 to 1 with difference 0.2, the test will look for combination parameter ??that get the minimum Makespan, The results of the implementation of the hybrid particle swarm optimization method and genetic algorithm are makespan of 29 days is obtained with an objective function value of 0.0043, with optimal parameters (C1) = 0.7, (C2) = 0.3, (w) = 0.3, (Cr) = 0.5, and (Mr) = 0.7.

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Published
2022-07-12
How to Cite
ADNYANA, Anak Agung Putra et al. Implementasi Metode Hybrid Particle Swarm Optimization dan Genetic Algorithm Pada Penjadwalan Job Shop Scheduling. JELIKU (Jurnal Elektronik Ilmu Komputer Udayana), [S.l.], v. 11, n. 3, p. 539-544, july 2022. ISSN 2654-5101. Available at: <https://ojs.unud.ac.id/index.php/jlk/article/view/88785>. Date accessed: 18 apr. 2024. doi: https://doi.org/10.24843/JLK.2023.v11.i03.p09.

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