Multisystem optimization for an integrated production scheduling with resource saving problem in textile printing and dyeing
Resource saving has become an integral aspect of manufacturing in industry 4.0. This paper proposes a multisystem optimization (MSO) algorithm, inspired by implicit parallelism of heuristic methods, to solve an integrated production scheduling with resource saving problem in textile printing and dye...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
2020
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| Online Access: | https://eprints.nottingham.ac.uk/64185/ |
| _version_ | 1848800100304814080 |
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| author | Ma, Haiping Sun, Chao Wang, Jinglin Yang, Zhile Zhou, Huiyu |
| author_facet | Ma, Haiping Sun, Chao Wang, Jinglin Yang, Zhile Zhou, Huiyu |
| author_sort | Ma, Haiping |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Resource saving has become an integral aspect of manufacturing in industry 4.0. This paper proposes a multisystem optimization (MSO) algorithm, inspired by implicit parallelism of heuristic methods, to solve an integrated production scheduling with resource saving problem in textile printing and dyeing. First, a real-world integrated production scheduling with resource saving is formulated as a multisystem optimization problem. Then, the MSO algorithm is proposed to solve multisystem optimization problems that consist of several coupled subsystems, and each of the subsystems may contain multiple objectives and multiple constraints. The proposed MSO algorithm is composed of within-subsystem evolution and cross-subsystem migration operators, and the former is to optimize each subsystem by excellent evolution operators and the later is to complete information sharing between multiple subsystems, to accelerate the global optimization of the whole system. Performance is tested on a set of multisystem benchmark functions and compared with improved NSGA-II and multiobjective multifactorial evolutionary algorithm (MO-MFEA). Simulation results show that the MSO algorithm is better than compared algorithms for the benchmark functions studied in this paper. Finally, the MSO algorithm is successfully applied to the proposed integrated production scheduling with resource saving problem, and the results show that MSO is a promising algorithm for the studied problem. © 2020 Haiping Ma et al. |
| first_indexed | 2025-11-14T20:46:11Z |
| format | Article |
| id | nottingham-64185 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:46:11Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-641852020-12-28T08:55:14Z https://eprints.nottingham.ac.uk/64185/ Multisystem optimization for an integrated production scheduling with resource saving problem in textile printing and dyeing Ma, Haiping Sun, Chao Wang, Jinglin Yang, Zhile Zhou, Huiyu Resource saving has become an integral aspect of manufacturing in industry 4.0. This paper proposes a multisystem optimization (MSO) algorithm, inspired by implicit parallelism of heuristic methods, to solve an integrated production scheduling with resource saving problem in textile printing and dyeing. First, a real-world integrated production scheduling with resource saving is formulated as a multisystem optimization problem. Then, the MSO algorithm is proposed to solve multisystem optimization problems that consist of several coupled subsystems, and each of the subsystems may contain multiple objectives and multiple constraints. The proposed MSO algorithm is composed of within-subsystem evolution and cross-subsystem migration operators, and the former is to optimize each subsystem by excellent evolution operators and the later is to complete information sharing between multiple subsystems, to accelerate the global optimization of the whole system. Performance is tested on a set of multisystem benchmark functions and compared with improved NSGA-II and multiobjective multifactorial evolutionary algorithm (MO-MFEA). Simulation results show that the MSO algorithm is better than compared algorithms for the benchmark functions studied in this paper. Finally, the MSO algorithm is successfully applied to the proposed integrated production scheduling with resource saving problem, and the results show that MSO is a promising algorithm for the studied problem. © 2020 Haiping Ma et al. 2020-11-18 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/64185/1/5.pdf Ma, Haiping, Sun, Chao, Wang, Jinglin, Yang, Zhile and Zhou, Huiyu (2020) Multisystem optimization for an integrated production scheduling with resource saving problem in textile printing and dyeing. Complexity, 2020 . pp. 1-14. ISSN 1076-2787 http://dx.doi.org/10.1155/2020/8853735 doi:10.1155/2020/8853735 doi:10.1155/2020/8853735 |
| spellingShingle | Ma, Haiping Sun, Chao Wang, Jinglin Yang, Zhile Zhou, Huiyu Multisystem optimization for an integrated production scheduling with resource saving problem in textile printing and dyeing |
| title | Multisystem optimization for an integrated production scheduling with resource saving problem in textile printing and dyeing |
| title_full | Multisystem optimization for an integrated production scheduling with resource saving problem in textile printing and dyeing |
| title_fullStr | Multisystem optimization for an integrated production scheduling with resource saving problem in textile printing and dyeing |
| title_full_unstemmed | Multisystem optimization for an integrated production scheduling with resource saving problem in textile printing and dyeing |
| title_short | Multisystem optimization for an integrated production scheduling with resource saving problem in textile printing and dyeing |
| title_sort | multisystem optimization for an integrated production scheduling with resource saving problem in textile printing and dyeing |
| url | https://eprints.nottingham.ac.uk/64185/ https://eprints.nottingham.ac.uk/64185/ https://eprints.nottingham.ac.uk/64185/ |