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) ; 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
Show full item record
Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.