LAPSE:2023.28141
Published Article
LAPSE:2023.28141
Evolutionary Process for Engineering Optimization in Manufacturing Applications: Fine Brushworks of Single-Objective to Multi-Objective/Many-Objective Optimization
April 11, 2023
Abstract
Single-objective to multi-objective/many-objective optimization (SMO) is a new paradigm in the evolutionary transfer optimization (ETO), since there are only “1 + 4” pioneering works on SMOs so far, that is, “1” is continuous and is firstly performed by Professors L. Feng and H.D. Wang, and “4” are firstly proposed by our group for discrete cases. As a new computational paradigm, theoretical insights into SMOs are relatively rare now. Therefore, we present a proposal on the fine brushworks of SMOs for theoretical advances here, which is based on a case study of a permutation flow shop scheduling problem (PFSP) in manufacturing systems via lenses of building blocks, transferring gaps, auxiliary task and asynchronous rhythms. The empirical studies on well-studied benchmarks enrich the rough strokes of SMOs and guide future designs and practices in ETO based manufacturing scheduling, and even ETO based evolutionary processes for engineering optimization in other cases.
Single-objective to multi-objective/many-objective optimization (SMO) is a new paradigm in the evolutionary transfer optimization (ETO), since there are only “1 + 4” pioneering works on SMOs so far, that is, “1” is continuous and is firstly performed by Professors L. Feng and H.D. Wang, and “4” are firstly proposed by our group for discrete cases. As a new computational paradigm, theoretical insights into SMOs are relatively rare now. Therefore, we present a proposal on the fine brushworks of SMOs for theoretical advances here, which is based on a case study of a permutation flow shop scheduling problem (PFSP) in manufacturing systems via lenses of building blocks, transferring gaps, auxiliary task and asynchronous rhythms. The empirical studies on well-studied benchmarks enrich the rough strokes of SMOs and guide future designs and practices in ETO based manufacturing scheduling, and even ETO based evolutionary processes for engineering optimization in other cases.
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Keywords
carbon neutrality, engineering optimization, evolutionary process, manufacturing applications, system optimization, transfer learning
Subject
Suggested Citation
Xu W, Wang X, Guo Q, Song X, Zhao R, Zhao G, Yang Y, Xu T, He D. Evolutionary Process for Engineering Optimization in Manufacturing Applications: Fine Brushworks of Single-Objective to Multi-Objective/Many-Objective Optimization. (2023). LAPSE:2023.28141
Author Affiliations
Xu W: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China; Frontier Science Center for Industrial In
Wang X: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China; Frontier Science Center for Industrial In
Guo Q: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China; Frontier Science Center for Industrial In [ORCID]
Song X: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China; Frontier Science Center for Industrial In
Zhao R: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China; Frontier Science Center for Industrial In
Zhao G: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China; Frontier Science Center for Industrial In
Yang Y: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China; Frontier Science Center for Industrial In
Xu T: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China; Frontier Science Center for Industrial In
He D: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China; Frontier Science Center for Industrial In
Wang X: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China; Frontier Science Center for Industrial In
Guo Q: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China; Frontier Science Center for Industrial In [ORCID]
Song X: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China; Frontier Science Center for Industrial In
Zhao R: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China; Frontier Science Center for Industrial In
Zhao G: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China; Frontier Science Center for Industrial In
Yang Y: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China; Frontier Science Center for Industrial In
Xu T: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China; Frontier Science Center for Industrial In
He D: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Shenyang 110819, China; Frontier Science Center for Industrial In
Journal Name
Processes
Volume
11
Issue
3
First Page
693
Year
2023
Publication Date
2023-02-24
ISSN
2227-9717
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PII: pr11030693, Publication Type: Journal Article
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LAPSE:2023.28141
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https://doi.org/10.3390/pr11030693
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Apr 11, 2023
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