Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Learning from Multi-Objective Optimization of Production Systems: A method for analyzing solution sets from multi-objective optimization
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. University of Skövde.
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 [en]
Data mining, Post-optimization analysis, Production system analysis
National Category
Computer and Information Science
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-99018ISBN: 978-91-7447-836-5 (print)OAI: oai:DiVA.org:su-99018DiVA: diva2:686155
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
List of papers
1. Information extraction from solution set of simulation-based multi-objective optimization using data mining
Open this publication in new window or tab >>Information extraction from solution set of simulation-based multi-objective optimization using data mining
2009 (English)In: Proceedings of Industrial Simulation Conference (ISC) 2009, 2009, 65-69 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this work, we investigate ways of extracting information from simulations, in particular from simulation-based multi-objective optimisation, in order to acquire information that can support human decision makers that aim for optimising manufacturing processes. Applying data mining for analyzing data generated using simulation is a fairly unexplored area. With the observation that the obtained solutions from a simulation-based multi-objective optimisation are all optimal (or close to the optimal Pareto front) so that they are bound to follow and exhibit certain relationships among variables vis-à-vis objectives, it is argued that using data mining to discover these relationships could be a promising procedure. The aim of this paper is to provide the empirical results from two simulation case studies to support such a hypothesis.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:su:diva-98567 (URN)
Conference
Industrial Simulation Conference 2009
Available from: 2014-01-08 Created: 2014-01-08 Last updated: 2014-01-10Bibliographically approved
2. A synergy of multi-objective optimization and data mining for the analysis of a flexible flow shop
Open this publication in new window or tab >>A synergy of multi-objective optimization and data mining for the analysis of a flexible flow shop
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
Keyword
Data mining, Decision trees, Post-optimality analysis, Simulation-based optimization
National Category
Engineering and Technology
Research subject
Technology
Identifiers
urn:nbn:se:su:diva-98756 (URN)10.1016/j.rcim.2010.12.005 (DOI)000291458900005 ()2-s2.0-79955664950 (Scopus ID)
Available from: 2011-05-02 Created: 2014-01-09 Last updated: 2017-12-06Bibliographically approved
3. Simulation-based innovization for manufacturing systems analysis using data mining and visual analytics
Open this publication in new window or tab >>Simulation-based innovization for manufacturing systems analysis using data mining and visual analytics
2011 (English)In: Proceedings of the 4th Swedish Production Symposium, 2011, 374-382 p.Conference paper, Published paper (Refereed)
National Category
Engineering and Technology
Identifiers
urn:nbn:se:su:diva-98752 (URN)
Conference
The 4th Swedish Production Symposium 3-5 May 2011, Lund
Available from: 2013-02-26 Created: 2014-01-09 Last updated: 2014-01-10Bibliographically approved
4. Simulation-based innovization using data mining for production systems analysis
Open this publication in new window or tab >>Simulation-based innovization using data mining for production systems analysis
2011 (English)In: Multi-objective Evolutionary Optimisation for Product Design and Manufacturing / [ed] Lihui Wang, Amos H. C. Ng, Kalyanmoy Deb, London: Springer, 2011, 401-429 p.Chapter in book (Refereed)
Abstract [en]

This chapter introduces a novel methodology for the analysis and optimization of production systems. The methodology is based on the innovization procedure, originally introduced for unveiling new and innovative design principles in engineering design problems. Although the innovization method is based on multi-objective optimization and post-optimality analyses of optimised solutions, it stretches the scope 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 problem can be obtained. By integrating the concept of innovization with discrete-event simulation and data mining techniques, a new set of powerful tools can be developed for general systems analysis, particularly suitable for production systems. The uniqueness of the integrated approach proposed in this chapter lies on applying data mining to the data sets generated from simulation-based multi-objective optimization, in order to automatically or semi-automatically discover and interpret the hidden relationships and patterns for optimal production systems design/reconfiguration.

Place, publisher, year, edition, pages
London: Springer, 2011
National Category
Computer and Information Science
Identifiers
urn:nbn:se:su:diva-99014 (URN)10.1007/978-0-85729-652-8_15 (DOI)978-0-85729-617-7 (ISBN)978-0-85729-652-8 (ISBN)
Available from: 2014-01-10 Created: 2014-01-10 Last updated: 2014-10-23Bibliographically approved
5. Integration of data mining and multi-objective optimisation for decision support in production systems development
Open this publication in new window or tab >>Integration of data mining and multi-objective optimisation for decision support in production systems development
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.

Keyword
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:nbn:se:su:diva-97219 (URN)10.1080/0951192X.2013.834481 (DOI)000337245200002 ()
Available from: 2013-12-05 Created: 2013-12-05 Last updated: 2017-12-06Bibliographically approved
6. Post-analysis of multi-objective optimization solutions using decision trees
Open this publication in new window or tab >>Post-analysis of multi-objective optimization solutions using decision trees
2015 (English)In: Intelligent Data Analysis, ISSN 1088-467X, E-ISSN 1571-4128, Vol. 19, no 2, 259-278 p.Article in journal (Refereed) Published
Abstract [en]

Evolutionary algorithms are often applied to solve multi-objective optimization problems. Such algorithms effectively generate solutions of wide spread, and have good convergence properties. However, they do not provide any characteristics of the found optimal solutions, something which may be very valuable to decision makers. By performing a post-analysis of the solution set from multi-objective optimization, relationships between the input space and the objective space can be identified. In this study, decision trees are used for this purpose. It is demonstrated that they may effectively capture important characteristics of the solution sets produced by multi-objective optimization methods. It is furthermore shown that the discovered relationships may be used for improving the search for additional solutions. Two multi-objective problems are considered in this paper; a well-studied benchmark function problem with on a beforehand known optimal Pareto front, which is used for verification purposes, and a multi-objective optimization problem of a real-world production system. The results show that useful relationships may be identified by employing decision tree analysis of the solution sets from multi-objective optimizations.

Keyword
Multi-objective optimization, post-optimality analysis, decision trees
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-117324 (URN)10.3233/IDA-150716 (DOI)000353062400004 ()
Available from: 2015-05-19 Created: 2015-05-18 Last updated: 2017-12-04Bibliographically approved
7. Knowledge discovery in production simulation by interleaving multi-objective optimization and data mining
Open this publication in new window or tab >>Knowledge discovery in production simulation by interleaving multi-objective optimization and data mining
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
Keyword
Production System Simulation, Multi-objective Optimization, Data Mining, Innovization
National Category
Computer and Information Science
Identifiers
urn:nbn:se:su:diva-99011 (URN)978-91-7519-752-4 (ISBN)
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

Open Access in DiVA

fulltext(6706 kB)476 downloads
File information
File name FULLTEXT01.pdfFile size 6706 kBChecksum SHA-512
2946ca02bc7daf06658d64259775d1f0198ebf1606a08c13cec7ad0215ed15c525b14d8a61908c577eca70ba08380e71555f6e3bb18e0a64bd4c5cbc2286e60c
Type fulltextMimetype application/pdf

By organisation
Department of Computer and Systems Sciences
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 476 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 666 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf