Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61399
Title: Remanufacturing-oriented process planning and scheduling: mathematical modelling and evolutionary optimisation
Contributor(s): Guiliang Gong (author); Deng, Qianwang (author); Chiong, Raymond  (author)orcid ; Gong, Xuran (author); Huang, Hezhiyuan (author); Han, Wenwu (author)
Publication Date: 2020
Early Online Version: 2019-06-27
DOI: 10.1080/00207543.2019.1634848
Handle Link: https://hdl.handle.net/1959.11/61399
Abstract: 

Remanufacturing has been widely studied for its potential to achieve sustainable production in recent years. In the literature of remanufacturing research, process planning and scheduling are typically treated as two independent parts. However, these two parts are in fact interrelated and often interact with each other. Doing process planning without considering scheduling related factors can easily introduce contradictions or even infeasible solutions. In this work, we propose a mathematical model of integrated process planning and scheduling for remanufacturing (IPPSR), which simultaneously considers the process planning and scheduling problems. An effective hybrid multi-objective evolutionary algorithm (HMEA) is presented to solve the proposed IPPSR. For the HMEA, a multidimensional encoding operator is designed to get a high-quality initial population. A multidimensional crossover operator and a multidimensional mutation operator are also proposed to improve the convergence speed of the algorithm and fully exploit the solution space. Finally, a specific legalising method is used to 'legalise' possible infeasible solutions generated by the initialisation method and mutation operator. Extensive computational experiments carried out to compare the HMEA with some well-known algorithms confirm that the proposed HMEA is able to obtain more and better Pareto solutions for IPPSR.

Publication Type: Journal Article
Source of Publication: International Journal of Production Research, 58(12), p. 3781-3799
Publisher: Taylor & Francis
Place of Publication: United Kingdom
ISSN: 1366-588X
0020-7543
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

23
checked on Jul 13, 2024
Google Media

Google ScholarTM

Check

Altmetric


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