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A synergy of multi-objective optimization and data mining for the analysis of a flexible flow shop
Högskolan i Skövde, Forskningscentrum för Virtuella system.
Högskolan i Skövde, Forskningscentrum för Virtuella system.
Högskolan i Skövde, Forskningscentrum för Virtuella system.
2011 (English)In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 27, no 4, 687-695 p.Article in journal (Refereed) Published
Abstract [en]

A method for analyzing production systems by applying multi-objective optimization and data mining techniques on discrete-event simulation models, the so-called Simulation-based Innovization (SBI) is presented in this paper. The aim of the SBI analysis is to reveal insight on the parameters that affect the performance measures as well as to gain deeper understanding of the problem, through post-optimality analysis of the solutions acquired from multi-objective optimization. This paper provides empirical results from an industrial case study, carried out on an automotive machining line, in order to explain the SBI procedure. The SBI method has been found to be particularly siutable in this case study as the three objectives under study, namely total tardiness, makespan and average work-in-process, are in conflict with each other. Depending on the system load of the line, different decision variables have been found to be influencing. How the SBI method is used to find important patterns in the explored solution set and how it can be valuable to support decision making in order to improve the scheduling under different system loadings in the machining line are addressed.

Place, publisher, year, edition, pages
Elsevier , 2011. Vol. 27, no 4, 687-695 p.
Keyword [en]
Data mining, Decision trees, Post-optimality analysis, Simulation-based optimization
National Category
Engineering and Technology
Research subject
Technology
Identifiers
URN: urn:nbn:se:su:diva-98756DOI: 10.1016/j.rcim.2010.12.005ISI: 000291458900005Scopus ID: 2-s2.0-79955664950OAI: oai:DiVA.org:su-98756DiVA: diva2:685342
Available from: 2011-05-02 Created: 2014-01-09 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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