Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61366
Title: A two-stage memetic algorithm for energy-efficient flexible job shop scheduling by means of decreasing the total number of machine restarts
Contributor(s): Gong, Guiliang (author); Chiong, Raymond  (author)orcid ; Deng, Qianwang (author); Gong, Xuran (author); Lin, Wenhui (author); Han, Wenwu (author); Zhang, Like (author)
Publication Date: 2022
DOI: 10.1016/j.swevo.2022.101131
Handle Link: https://hdl.handle.net/1959.11/61366
Abstract: 

Machine on/off control is an effective way to achieve energy-efficient production scheduling. Turning off machines and restarting them frequently, however, would incur a considerable amount of additional energy and may even cause damage to the machines. In this paper, we propose a mathematical model based on the energyefficient flexible job shop scheduling problem (EEFJSP), aiming to minimize not just the makespan and total energy consumption but also the total number of machine restarts. Our idea here is that shifting the start time of operations on different machines appropriately can effectively decrease the number of restarts required and the total energy consumption. We present a two-stage memetic algorithm (TMA) to solve the EEFJSP. A variable neighborhood search approach is designed to improve the convergence speed and fully exploit the solution space of the TMA. An operation-block moving operator is developed to further reduce the total energy consumption as well as the total number of machine restarts without affecting the makespan. Extensive computational experiments carried out to compare the TMA with some well-known algorithms confirm that the proposed TMA can easily obtain better Pareto solutions for the EEFJSP.

Publication Type: Journal Article
Source of Publication: Swarm and Evolutionary Computation, v.75, p. 1-18
Publisher: Elsevier BV
Place of Publication: The Netherlands
ISSN: 2210-6510
2210-6502
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

Files in This Item:
1 files
File SizeFormat 
Show full item record

SCOPUSTM   
Citations

21
checked on Sep 14, 2024

Page view(s)

352
checked on Aug 3, 2024
Google Media

Google ScholarTM

Check

Altmetric


Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.