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Knowledge discovery in production simulation by interleaving multi-objective optimization and data mining
University of Skövde.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. University of Skövde.
University of Skövde.
University of Skövde.
2012 (English)In: Proceedings of the SPS12 conference 2012, The Swedish Production Academy , 2012, 461-471 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper introduces a novel methodology for the optimization, analysis and decision support in production systems development. The methodology is based on the innovization procedure, originally introduced for unveiling new and innovative design principles in engineering design problems. The innovization (innovation via optimization) procedure stretches beyond an optimization task and attempts to discover new design/operational rules/principles relating to decision variables and objectives, so that a deeper understanding of the underlying problem can be obtained. By integrating the concept of innovization with simulation and data mining DM techniques, a new set of powerful tools can be developed for general systems analysis. The uniqueness of the approach introduced in this paper lies on the decision rules extracted from the multi-objective optimization (MOO) using data mining (DM) are used to modify the original optimization so that faster convergence to the desired solution of the decision maker can be achieved. In other words, faster convergence and deeper knowledge of the relationships between the key decision variables and objectives can be obtained by interleaving the MOO and DM processes. In this paper, such an interleaved approach is illustrated through a set of experiments carried out to a simulation model developed in a real-world production system improvement project.

Place, publisher, year, edition, pages
The Swedish Production Academy , 2012. 461-471 p.
Keyword [en]
Production System Simulation, Multi-objective Optimization, Data Mining, Innovization
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:su:diva-99011ISBN: 978-91-7519-752-4 (print)OAI: oai:DiVA.org:su-99011DiVA: diva2:686120
Conference
The 5th International Swedish Production Symposium 6th – 8th of November 2012 Linköping, Sweden
Available from: 2014-01-10 Created: 2014-01-10 Last updated: 2016-02-05Bibliographically 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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