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Optimization of cost-based hybrid flowshop scheduling using teaching-learning-based optimization algorithm

A cost-based hybrid flowshop scheduling (CHFS) combines flow shop and job shop elements, with cost considerations as a key indicator. CHFS is a complex combinatorial optimization challenge encountered in real-world manufacturing and production environments. This paper investigates the optimization o...

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Main Authors: Ullah, Wasif, Muhammad Ammar, Nik Mu’tasim, Mohd Fadzil Faisae, Ab Rashid
Format: Article
Language:English
Published: Universiti Malaysia Pahang 2024
Subjects:
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/42704/1/document.pdf
http://umpir.ump.edu.my/id/eprint/42704/
https://doi.org/10.15282/ijame.21.3.2024.13.0896
https://doi.org/10.15282/ijame.21.3.2024.13.0896
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spelling my.ump.umpir.427042024-10-02T07:25:54Z http://umpir.ump.edu.my/id/eprint/42704/ Optimization of cost-based hybrid flowshop scheduling using teaching-learning-based optimization algorithm Ullah, Wasif Muhammad Ammar, Nik Mu’tasim Mohd Fadzil Faisae, Ab Rashid TS Manufactures A cost-based hybrid flowshop scheduling (CHFS) combines flow shop and job shop elements, with cost considerations as a key indicator. CHFS is a complex combinatorial optimization challenge encountered in real-world manufacturing and production environments. This paper investigates the optimization of a CHFS problem using the Teaching Learning-Based Optimization (TLBO) algorithm. Effective CHFS is crucial for achieving production balance, reducing costs, and improving customer satisfaction. The authors formulate the CHFS scheduling problem and propose applying the TLBO algorithm to minimize total costs, including labor, energy, maintenance, and delay expenses. The performance of the TLBO technique is evaluated through computational experiments on various CHFS problem instances. The results demonstrate the effectiveness of the TLBO algorithm, which achieved the best results in 42% of the test cases, surpassing other algorithms like the Grey Wolf Optimizer and Particle Swarm Optimization. Additionally, the TLBO algorithm had the highest average performance ranking across the comparative algorithms. The study highlights the potential of the TLBO algorithm as an efficient optimization tool for complex manufacturing scheduling problems. Universiti Malaysia Pahang 2024-09 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/42704/1/document.pdf Ullah, Wasif and Muhammad Ammar, Nik Mu’tasim and Mohd Fadzil Faisae, Ab Rashid (2024) Optimization of cost-based hybrid flowshop scheduling using teaching-learning-based optimization algorithm. International Journal of Automotive and Mechanical Engineering (IJAME), 21 (3). pp. 11616-11628. ISSN 1985-9325(Print); 2180-1606 (Online). (Published) https://doi.org/10.15282/ijame.21.3.2024.13.0896 https://doi.org/10.15282/ijame.21.3.2024.13.0896
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TS Manufactures
spellingShingle TS Manufactures
Ullah, Wasif
Muhammad Ammar, Nik Mu’tasim
Mohd Fadzil Faisae, Ab Rashid
Optimization of cost-based hybrid flowshop scheduling using teaching-learning-based optimization algorithm
description A cost-based hybrid flowshop scheduling (CHFS) combines flow shop and job shop elements, with cost considerations as a key indicator. CHFS is a complex combinatorial optimization challenge encountered in real-world manufacturing and production environments. This paper investigates the optimization of a CHFS problem using the Teaching Learning-Based Optimization (TLBO) algorithm. Effective CHFS is crucial for achieving production balance, reducing costs, and improving customer satisfaction. The authors formulate the CHFS scheduling problem and propose applying the TLBO algorithm to minimize total costs, including labor, energy, maintenance, and delay expenses. The performance of the TLBO technique is evaluated through computational experiments on various CHFS problem instances. The results demonstrate the effectiveness of the TLBO algorithm, which achieved the best results in 42% of the test cases, surpassing other algorithms like the Grey Wolf Optimizer and Particle Swarm Optimization. Additionally, the TLBO algorithm had the highest average performance ranking across the comparative algorithms. The study highlights the potential of the TLBO algorithm as an efficient optimization tool for complex manufacturing scheduling problems.
format Article
author Ullah, Wasif
Muhammad Ammar, Nik Mu’tasim
Mohd Fadzil Faisae, Ab Rashid
author_facet Ullah, Wasif
Muhammad Ammar, Nik Mu’tasim
Mohd Fadzil Faisae, Ab Rashid
author_sort Ullah, Wasif
title Optimization of cost-based hybrid flowshop scheduling using teaching-learning-based optimization algorithm
title_short Optimization of cost-based hybrid flowshop scheduling using teaching-learning-based optimization algorithm
title_full Optimization of cost-based hybrid flowshop scheduling using teaching-learning-based optimization algorithm
title_fullStr Optimization of cost-based hybrid flowshop scheduling using teaching-learning-based optimization algorithm
title_full_unstemmed Optimization of cost-based hybrid flowshop scheduling using teaching-learning-based optimization algorithm
title_sort optimization of cost-based hybrid flowshop scheduling using teaching-learning-based optimization algorithm
publisher Universiti Malaysia Pahang
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/42704/1/document.pdf
http://umpir.ump.edu.my/id/eprint/42704/
https://doi.org/10.15282/ijame.21.3.2024.13.0896
https://doi.org/10.15282/ijame.21.3.2024.13.0896
_version_ 1822924698820804608
score 13.235362

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