Multisystem Optimization for an Integrated Production Scheduling with Resource Saving Problem in Textile Printing and Dyeing

Haiping Ma*, Chao Sun, Jinglin Wang, Zhile Yang, Huiyu Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number8853735
JournalComplexity
Volume2020
DOIs
Publication statusPublished - 18 Nov 2020
Externally publishedYes

Bibliographical note

Funding Information:
This research was supported by the Zhejiang Provincial Natural Science Foundation of China under grant no. LY19F030011, National Natural Science Foundation of China under grant nos. 52077213 and 62003332, and Natural Science Foundation of Guangdong Province under grant no. 2018A030310671. H. Zhou was supported by the UK EPSRC under grant no. EP/N011074/1, Royal Society-Newton Advanced Fellowship under grant no. NA160342, and European Union's Horizon 2020 Research and Innovation Program under the Marie-Sklodowska-Curie grant agreement no. 720325.

Publisher Copyright:
© 2020 Haiping Ma et al.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

ASJC Scopus subject areas

  • Computer Science(all)
  • General

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