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Integration of data mining and multi-objective optimisation for decision support in production systems development
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2014 (English)In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 27, no 9, 824-839 p.Article in journal (Refereed) Published
Abstract [en]

Multi-objective optimisation (MOO) is a powerful approach for generating a set of optimal trade-off (Pareto) design alternatives that the decision-maker can evaluate and then choose the most-suitable configuration, based on some high-level strategic information. Nevertheless, in practice, choosing among a large number of solutions on the Pareto front is often a daunting task, if proper analysis and visualisation techniques are not applied. Recent research advancements have shown the advantages of using data mining techniques to automate the post-optimality analysis of Pareto-optimal solutions for engineering design problems. Nonetheless, it is argued that the existing approaches are inadequate for generating high-quality results, when the set of the Pareto solutions is relatively small and the solutions close to the Pareto front have almost the same attributes as the Pareto-optimal solutions, of which both are commonly found in many real-world system problems. The aim of this paper is therefore to propose a distance-based data mining approach for the solution sets generated from simulation-based optimisation, in order to address these issues. Such an integrated data mining and MOO procedure is illustrated with the results of an industrial cost optimisation case study. Particular emphasis is paid to showing how the proposed procedure can be used to assist decision-makers in analysing and visualising the attributes of the design alternatives in different regions of the objective space, so that informed decisions can be made in production systems development.

Place, publisher, year, edition, pages
2014. Vol. 27, no 9, 824-839 p.
Keyword [en]
data mining, post\-optimality analysis, multi\-objective optimisation, decision trees, production systems development
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-97219DOI: 10.1080/0951192X.2013.834481ISI: 000337245200002OAI: oai:DiVA.org:su-97219DiVA: diva2:676263
Available from: 2013-12-05 Created: 2013-12-05 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Learning from Multi-Objective Optimization of Production Systems: A method for analyzing solution sets from multi-objective optimization
Open this publication in new window or tab >>Learning from Multi-Objective Optimization of Production Systems: A method for analyzing solution sets from multi-objective optimization
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The process of multi-objective optimization involves finding optimal solutions to several objective functions. However, these are typically in conflict with each other in many real-world problems, such as production system design. Advanced post-optimization analysis can be used to provide the decision maker with information about the underlying system. The analysis can be based on the combination of simulation-based multi-objective optimization and learning from the obtained solution set. The goal of the analysis is to gain a deeper understanding of the problem at hand, to systematically explore and evaluate different alternatives, and to generate essential information and knowledge to support the decision maker to make more informed decisions in order to optimize the performance of the production system as a whole.

The aim of this work is to explore the possibilities on how post-optimization analysis can be used in order to provide the decision maker with essential information about an underlying system and in what way this information can be presented. The analysis is mainly done on production system development problems, but may also be transferred to other application areas.

The research process of the thesis has been iterative, and the initial approach for post-optimization analysis has been refined several times. The distance-based approach developed in the thesis is used to allow the extraction of information about the characteristics close to a user-defined reference point. The extracted rules are presented to the decision maker both visually, by mapping the rules to the objective space, and textually. The method has been applied to several industrial cases for proof-by-demonstration as well as to an artificial case with information known beforehand to verify the distance-based approach, and the extracted rules have also been used to limit the search space in the optimization. The major finding in the thesis is that to learn from optimization solution sets of production system problems with stochastic behavior, a distance-based approach is advantageous compared with a binary classification of optimal vs. non-optimal solutions.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2014. 109 p.
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 14-002
Keyword
Data mining, Post-optimization analysis, Production system analysis
National Category
Computer and Information Science
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-99018 (URN)978-91-7447-836-5 (ISBN)
Public defence
2014-03-14, Sal A, Forum, Isafjordsgatan 39, Kista, 13:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defence the following articles were unpublished and had a status as follows: Paper 5: Epubl ahead of print; Paper 6: Accepted.

Available from: 2014-01-20 Created: 2014-01-10 Last updated: 2015-12-01Bibliographically approved

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