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Online Knowledge Ex...
Online Knowledge Extraction and Preference Guided Multi-Objective Optimization in Manufacturing
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- Karlsson, Ingemar (author)
- Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningsmiljön Virtuell produkt- och produktionsutveckling,Production and Automation Engineering,Univ Skövde, Sch Engn Sci, S-54128 Skövde, Sweden.
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- Bandaru, Sunith, 1984- (author)
- Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningsmiljön Virtuell produkt- och produktionsutveckling,Production and Automation Engineering,Univ Skövde, Sch Engn Sci, S-54128 Skövde, Sweden.
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- Ng, Amos H. C., 1970- (author)
- Uppsala universitet,Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningsmiljön Virtuell produkt- och produktionsutveckling,Department of Civil and Industrial Engineering, Uppsala University, Swede,Production and Automation Engineering,Industriell teknik,Univ Skövde, Sch Engn Sci, S-54128 Skövde, Sweden
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(creator_code:org_t)
- IEEE, 2021
- 2021
- English.
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In: IEEE Access. - : IEEE. - 2169-3536. ; 9, s. 145382-145396
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Abstract
Subject headings
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- The integration of simulation-based optimization and data mining is an emerging approach to support decision-making in the design and improvement of manufacturing systems. In such an approach, knowledge extracted from the optimal solutions generated by the simulation-based optimization process can provide important information to decision makers, such as the importance of the decision variables and their influence on the design objectives, which cannot easily be obtained by other means. However, can the extracted knowledge be directly used during the optimization process to further enhance the quality of the solutions? This paper proposes such an online knowledge extraction approach that is used together with a preference-guided multi-objective optimization algorithm on simulation models of manufacturing systems. Specifically, it introduces a combination of the multi-objective evolutionary optimization algorithm, NSGA-II, and a customized data mining algorithm, called Flexible Pattern Mining (FPM), which can extract knowledge in the form of rules in an online and automatic manner, in order to guide the optimization to converge towards a decision maker's preferred region in the objective space. Through a set of application problems, this paper demonstrates how the proposed FPM-NSGA-II can be used to support higher quality decision-making in manufacturing.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Produktionsteknik, arbetsvetenskap och ergonomi (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Production Engineering, Human Work Science and Ergonomics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Optimization
- Data mining
- Evolutionary computation
- Decision making
- Tools
- Licenses
- Biological system modeling
- Manufacturing
- simulation-based optimization
- evolutionary algorithms
- Production and Automation Engineering
- Produktion och automatiseringsteknik
- VF-KDO
- VF-KDO
Publication and Content Type
- ref (subject category)
- art (subject category)
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