Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/544
Title: A new hybrid ant colony optimization algorithm for solving the no-wait flow shop scheduling problems
Authors: Engin, Orhan
Güçlü, Abdullah
Keywords: Scheduling
No-Wait Flow Shop
Hybrid Ant Colony Algorithm
Makespan
Particle Swarm Optimization
Iterated Greedy Algorithm
Total Completion-Time
Makespan Criterion
Genetic Algorithms
Minimize Makespan
Setup Times
In-Process
Flowshops
Search
Publisher: ELSEVIER SCIENCE BV
Abstract: This paper proposes an effective new hybrid ant colony algorithm based on crossover and mutation mechanism for no-wait flow shop scheduling with the criterion to minimize the maximum completion time. The no-wait flow shop is known as a typical NP-hard combinational optimization problem. The hybrid ant colony algorithm is applied to the 192 benchmark instances from literature in order to minimize makespan. The performance of the proposed Hybrid Ant Colony algorithm is compared to the Adaptive Learning Approach and Genetic Heuristic algorithm which are used in previous studies to solve the same set of benchmark problems. The computational experiments show that the proposed Hybrid Ant Colony algorithm provides better results relative to the other algorithms. (C) 2018 Elsevier B.V. All rights reserved.
URI: https://doi.org/10.1016/j.asoc.2018.08.002
https://hdl.handle.net/20.500.13091/544
ISSN: 1568-4946
1872-9681
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

Files in This Item:
File SizeFormat 
1-s2.0-S1568494618304502-main.pdf
  Until 2030-01-01
1.4 MBAdobe PDFView/Open    Request a copy
Show full item record



CORE Recommender

SCOPUSTM   
Citations

109
checked on Apr 20, 2024

WEB OF SCIENCETM
Citations

113
checked on Apr 20, 2024

Page view(s)

158
checked on Apr 22, 2024

Download(s)

6
checked on Apr 22, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.