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Innovative design and analysis of production systems by multi-objective optimization and data mining
Jönköping University, School of Engineering, JTH, Industrial Engineering and Management. Production and Automation Engineering, School of Engineering Science, University of Skövde, Sweden.
Production and Automation Engineering, School of Engineering Science, University of Skövde, Sweden.
Production and Automation Engineering, School of Engineering Science, University of Skövde, Sweden.
2016 (English)In: Procedia CIRP, 2016, 665-671 p.Conference paper (Refereed)
Abstract [en]

This paper presents an innovative approach for the design and analysis of production systems using multi-objective optimization and data mining. The innovation lies on how these two methods using different computational intelligence algorithms can be synergistically integrated and used interactively by production systems designers to support their design decisions. Unlike ordinary optimization approaches for production systems design which several design objectives are linearly combined into a single mathematical function, multi-objective optimization that can generate multiple design alternatives and sort their performances into an efficient frontier can enable the designer to have a more complete picture about how the design decision variables, like number of machines and buffers, can affect the overall performances of the system. Such kind of knowledge that can be gained by plotting the efficient frontier cannot be sought by single-objective based optimizations. Additionally, because of the multiple optimal design alternatives generated, they constitute a dataset that can be fed into some data mining algorithms for extracting the knowledge about the relationships among the design variables and the objectives. This paper addresses the specific challenges posed by the design of discrete production systems for this integrated optimization and data mining approach and then outline a new interactive data mining algorithm developed to meet these challenges, illustrated with a real-world production line design example.

Place, publisher, year, edition, pages
2016. 665-671 p.
Keyword [en]
Data Mining, Multi-Objective Optimization, Production Systems, Algorithms, Artificial intelligence, Design, Functions, Multiobjective optimization, Optimization, Systems analysis, Computational Intelligence algorithms, Data mining algorithm, Integrated optimization, Interactive data mining, Mathematical functions, Optimization approach, Production line design, Production system
National Category
Computer and Information Science Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:hj:diva-31871DOI: 10.1016/j.procir.2016.04.159ScopusID: 2-s2.0-84986608440OAI: oai:DiVA.org:hj-31871DiVA: diva2:974359
Conference
26th CIRP Design Conference, 2016, 15 June 2016 through 17 June 2016
Available from: 2016-09-26 Created: 2016-09-26 Last updated: 2016-09-26Bibliographically approved

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