Development of Guided Artificial Bee Colony (GABC) Heuristic for Permutation Flowshop Scheduling Problem (PFSP)

Authors

  • Noor Azizah Sidek Department of Mechanical Engineering, Centre for Diploma Studies, Universiti Tun Hussein Onn Malaysia, Pagoh Higher Education Hub, Jalan Panchor, 84600 Panchor, Johor, Malaysia
  • Salleh Ahmad Bareduan Department of Manufacturing Engineering, Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
  • Azli Nawawi Department of Mechanical Engineering Technology, Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, Pagoh Higher Education Hub, Jalan Panchor, 84600 Panchor, Johor, Malaysia
  • Ten Jia Yee Department of Manufacturing Engineering, Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia

DOI:

https://doi.org/10.37934/araset.33.3.393406

Keywords:

Guided Artificial Bee Colony Algorithm, flowshop scheduling, scheduling optimization

Abstract

The flowshop is the most often used production system in the sector, and several efforts have been made to improve its efficiency. The NEH (Nawaz, Enscore and Ham) heuristics are one of the promising techniques. The range includes using heuristics and metaheuristics. By adopting a modified version of the Artificial Bee Colony (ABC) algorithm, which has the disadvantage of a slow converge speed, this study aims to boost NEH. To find high-quality results with a faster convergence rate, this study developed a strategy to increase the convergence speed of ABC. Because of the significant performance in the makespan value (performance indicator), the Total Greedy was adopted in this study, and the author continued to use it throughout the remainder of the research. This study suggested creating a Guided Artificial Bee Colony (GABC) using the First Job Sequence Arrangement Method and the NEH idea. The investigation was based on Taillard benchmark datasets. According to the findings, ABC frequently gave inconsistent outcomes, but surprisingly, GABC, NEH-based ABC, and ABC consistently produced results that were each 68.75%, 63.33%, and 0.01% better than NEH. Finally, the author can state that this analysis validated ABC's slow convergence problem solutions.

Author Biographies

Noor Azizah Sidek, Department of Mechanical Engineering, Centre for Diploma Studies, Universiti Tun Hussein Onn Malaysia, Pagoh Higher Education Hub, Jalan Panchor, 84600 Panchor, Johor, Malaysia

noorazizah@uthm.edu.my

Salleh Ahmad Bareduan , Department of Manufacturing Engineering, Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia

saleh@uthm.edu.my

Azli Nawawi , Department of Mechanical Engineering Technology, Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, Pagoh Higher Education Hub, Jalan Panchor, 84600 Panchor, Johor, Malaysia

azle@uthm.edu.my

Ten Jia Yee , Department of Manufacturing Engineering, Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia

hd210035@student.uthm.edu.my

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Published

2023-11-22

How to Cite

Noor Azizah Sidek, Salleh Ahmad Bareduan, Azli Nawawi, & Ten Jia Yee. (2023). Development of Guided Artificial Bee Colony (GABC) Heuristic for Permutation Flowshop Scheduling Problem (PFSP). Journal of Advanced Research in Applied Sciences and Engineering Technology, 33(3), 393–406. https://doi.org/10.37934/araset.33.3.393406

Issue

Vol. 33 No. 3: February (2024)

Section

Articles