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Siegmund, F., Ng, A. H. C. & Deb, K. (2017). A Comparative Study of Fast Adaptive Preference-Guided Evolutionary Multi-objective Optimization. In: Heike Trautmann, Rudolph Günter, Kathrin Klamroth, Oliver Schütze, Margaret Wiecek, Yaochu Jin, and Christian Grimme (Ed.), Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings. Paper presented at 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017 (pp. 560-574). Springer, 10173.
Open this publication in new window or tab >>A Comparative Study of Fast Adaptive Preference-Guided Evolutionary Multi-objective Optimization
2017 (English)In: Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings / [ed] Heike Trautmann, Rudolph Günter, Kathrin Klamroth, Oliver Schütze, Margaret Wiecek, Yaochu Jin, and Christian Grimme, Springer, 2017, Vol. 10173, p. 560-574Conference paper, Published paper (Refereed)
Abstract [en]

In Simulation-based Evolutionary Multi-objective Optimization, the number of simulation runs is very limited, since the complex simulation models require long execution times. With the help of preference information, the optimization result can be improved by guiding the optimization towards relevant areas in the objective space with, for example, the Reference Point-based NSGA-II algorithm (R-NSGA-II). Since the Pareto-relation is the primary fitness function in R-NSGA-II, the algorithm focuses on exploring the objective space with high diversity. Only after the population has converged closeto the Pareto-front does the influence of the reference point distance as secondary fitness criterion increase and the algorithm converges towards the preferred area on the Pareto-front.In this paper, we propose a set of extensions of R-NSGA-II which adaptively control the algorithm behavior, in order to converge faster towards the reference point. The adaption can be based on criteria such as elapsed optimization time or the reference point distance, or a combination thereof. In order to evaluate the performance of the adaptive extensions of R-NSGA-II, a performance metric for reference point-based EMO algorithms is used, which is based on the Hypervolume measure called the Focused Hypervolume metric. It measures convergence and diversity of the population in the preferred area around the reference point. The results are evaluated on two benchmark problems ofdifferent complexity and a simplistic production line model.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743, E-ISSN 1611-3349 ; 10173
Keyword
Evolutionary multi-objective optimization, Guided search, Preference-guided EMO, Reference point, Decision support, Adaptive
National Category
Computer Sciences
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-13448 (URN)10.1007/978-3-319-54157-0_38 (DOI)2-s2.0-85014258475 (Scopus ID)978-3-319-54156-3 (ISBN)978-3-319-54157-0 (ISBN)
Conference
9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017
Funder
Knowledge Foundation
Available from: 2017-03-24 Created: 2017-03-24 Last updated: 2018-01-13Bibliographically approved
Bandaru, S., Ng, A. H. C. & Deb, K. (2017). Data mining methods for knowledge discovery in multi-objective optimization: Part A - Survey. Expert systems with applications, 70, 139-159.
Open this publication in new window or tab >>Data mining methods for knowledge discovery in multi-objective optimization: Part A - Survey
2017 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 70, p. 139-159Article, review/survey (Refereed) Published
Abstract [en]

Real-world optimization problems typically involve multiple objectives to be optimized simultaneously under multiple constraints and with respect to several variables. While multi-objective optimization itself can be a challenging task, equally difficult is the ability to make sense of the obtained solutions. In this two-part paper, we deal with data mining methods that can be applied to extract knowledge about multi-objective optimization problems from the solutions generated during optimization. This knowledge is expected to provide deeper insights about the problem to the decision maker, in addition to assisting the optimization process in future design iterations through an expert system. The current paper surveys several existing data mining methods and classifies them by methodology and type of knowledge discovered. Most of these methods come from the domain of exploratory data analysis and can be applied to any multivariate data. We specifically look at methods that can generate explicit knowledge in a machine-usable form. A framework for knowledge-driven optimization is proposed, which involves both online and offline elements of knowledge discovery. One of the conclusions of this survey is that while there are a number of data mining methods that can deal with data involving continuous variables, only a few ad hoc methods exist that can provide explicit knowledge when the variables involved are of a discrete nature. Part B of this paper proposes new techniques that can be used with such datasets and applies them to discrete variable multi-objective problems related to production systems. 

Keyword
Data mining, Multi-objective optimization, Descriptive statistics, Visual data mining, Machine learning, Knowledge-driven optimization
National Category
Computer Sciences
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-13267 (URN)10.1016/j.eswa.2016.10.015 (DOI)000389162000009 ()2-s2.0-84995972531 (Scopus ID)
Projects
KDISCO and Knowledge Driven Decision Support via Optimization (KDDS)
Funder
Knowledge Foundation, 41231
Available from: 2016-12-29 Created: 2016-12-29 Last updated: 2018-01-13Bibliographically approved
Bandaru, S., Ng, A. H. C. & Deb, K. (2017). Data mining methods for knowledge discovery in multi-objective optimization: Part B - New developments and applications. Expert systems with applications, 70, 119-138.
Open this publication in new window or tab >>Data mining methods for knowledge discovery in multi-objective optimization: Part B - New developments and applications
2017 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 70, p. 119-138Article in journal (Refereed) Published
Abstract [en]

The first part of this paper served as a comprehensive survey of data mining methods that have been used to extract knowledge from solutions generated during multi-objective optimization. The current paper addresses three major shortcomings of existing methods, namely, lack of interactiveness in the objective space, inability to handle discrete variables and inability to generate explicit knowledge. Four data mining methods are developed that can discover knowledge in the decision space and visualize it in the objective space. These methods are (i) sequential pattern mining, (ii) clustering-based classification trees, (iii) hybrid learning, and (iv) flexible pattern mining. Each method uses a unique learning strategy to generate explicit knowledge in the form of patterns, decision rules and unsupervised rules. The methods are also capable of taking the decision maker's preferences into account to generate knowledge unique to preferred regions of the objective space. Three realistic production systems involving different types of discrete variables are chosen as application studies. A multi-objective optimization problem is formulated for each system and solved using NSGA-II to generate the optimization datasets. Next, all four methods are applied to each dataset. In each application, the methods discover similar knowledge for specified regions of the objective space. Overall, the unsupervised rules generated by flexible pattern mining are found to be the most consistent, whereas the supervised rules from classification trees are the most sensitive to user-preferences. 

Keyword
Data mining, Knowledge discovery, Multi-objective optimization, Discrete variables, Production systems, Flexible pattern mining
National Category
Computer Sciences
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-13266 (URN)10.1016/j.eswa.2016.10.016 (DOI)000389162000008 ()2-s2.0-84995977095 (Scopus ID)
Projects
KDISCO and Knowledge Driven Decision Support via Optimization (KDDS)
Funder
Knowledge Foundation, 41231
Available from: 2016-12-29 Created: 2016-12-29 Last updated: 2018-01-13Bibliographically approved
Goienetxea Uriarte, A., Ruiz Zúñiga, E., Urenda Moris, M. & Ng, A. H. C. (2017). How can decision makers be supported in the improvement of an emergency department?: A simulation, optimization and data mining approach. Operations Research for Health Care, 15, 102-122.
Open this publication in new window or tab >>How can decision makers be supported in the improvement of an emergency department?: A simulation, optimization and data mining approach
2017 (English)In: Operations Research for Health Care, ISSN 2211-6923, E-ISSN 2211-6931, Vol. 15, p. 102-122Article in journal (Refereed) Published
Abstract [en]

The improvement of emergency department processes involves the need to take into considerationmultiple variables and objectives in a highly dynamic and unpredictable environment, which makes thedecision-making task extremely challenging. The use of different methodologies and tools to support thedecision-making process is therefore a key issue. This article presents a novel approach in healthcarein which Discrete Event Simulation, Simulation-Based Multi-Objective Optimization and Data Miningtechniques are used in combination. This methodology has been applied for a system improvementanalysis in a Swedish emergency department. As a result of the project, the decision makers were providedwith a range of nearly optimal solutions and design rules which reduce considerably the length of stayand waiting times for emergency department patients. These solutions include the optimal number ofresources and the required level of improvement in key processes. The article presents and discussesthe benefits achieved by applying this methodology, which has proven to be remarkably valuable fordecision-making support, with regard to complex healthcare system design and improvement.

Place, publisher, year, edition, pages
Elsevier, 2017
Keyword
Discrete Event Simulation, Simulation-Based Multi-Objective Optimization, Data mining, Decision support, Decision-making, Operational research in health care
National Category
Health Care Service and Management, Health Policy and Services and Health Economy Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-14404 (URN)10.1016/j.orhc.2017.10.003 (DOI)000415311000010 ()
Available from: 2017-11-15 Created: 2017-11-15 Last updated: 2018-02-01Bibliographically approved
Goienetxea Uriarte, A., Ng, A. H. C., Ruiz Zúñiga, E. & Urenda Moris, M. (2017). Improving the Material Flow of a Manufacturing Company via Lean, Simulation and Optimization. In: Proceedings of the International Conference on Industrial Engineering and Engineering Management, IEEM2017: . Paper presented at 2017 International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, December 10-13, 2017 (pp. 1245-1250). IEEE.
Open this publication in new window or tab >>Improving the Material Flow of a Manufacturing Company via Lean, Simulation and Optimization
2017 (English)In: Proceedings of the International Conference on Industrial Engineering and Engineering Management, IEEM2017, IEEE, 2017, p. 1245-1250Conference paper, Published paper (Refereed)
Abstract [en]

Companies are continuously working towards system and process improvement to remain competitive in aglobal market. There are different methods that support companies in the achievement of that goal. This paper presents an innovative process that combines lean, simulation and optimization to improve the material flow of a manufacturing company. A description of each step of the process details the lean tools and principles taken into account, as well as the results achieved by the application of simulation and optimization.The project resulted in an improved layout and material flow that employs an automated guided vehicle. In addition, lean wastes related to transport, inventory levels as well as waiting times were reduced. The utilization of the process that combines lean, simulation and optimization was considered valuable for the success of the project.

Place, publisher, year, edition, pages
IEEE, 2017
Series
International Conference on Industrial Engineering and Engineering Management, E-ISSN 2157-362X
Keyword
Application study, lean, manufacturing, optimization, simulation, simulation-based optimization
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-14688 (URN)10.1109/IEEM.2017.8290092 (DOI)978-1-5386-0948-4 (ISBN)978-1-5386-0947-7 (ISBN)978-1-5386-0949-1 (ISBN)
Conference
2017 International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, December 10-13, 2017
Available from: 2018-01-25 Created: 2018-01-25 Last updated: 2018-02-22
Linnéusson, G., Ng, A. H. C. & Aslam, T. (2017). Justifying Maintenance Studying System Behavior: A Multipurpose Approach Using Multi-objective Optimization. In: Sterman, J. and Repenning, N. (Ed.), 35th International Conference of the System Dynamics Society 2017: Cambridge, Massachusetts, USA 16 - 20 July 2017. Paper presented at 35th International Conference of the System Dynamics Society, Cambridge, Massachusetts, USA, July 16-20, 2017 (pp. 1061-1081). Curran Associates, Inc., 2.
Open this publication in new window or tab >>Justifying Maintenance Studying System Behavior: A Multipurpose Approach Using Multi-objective Optimization
2017 (English)In: 35th International Conference of the System Dynamics Society 2017: Cambridge, Massachusetts, USA 16 - 20 July 2017 / [ed] Sterman, J. and Repenning, N., Curran Associates, Inc., 2017, Vol. 2, p. 1061-1081Conference paper, Published paper (Refereed)
Abstract [en]

Industrial maintenance includes rich internaldynamic complexity on how to deliver value. While the technical development hasprovided with applicable solutions in terms of reliability and condition basedmonitoring, managing maintenance is still an act of balancing, trying to pleasethe short-termism from the economic requirements and simultaneously address thenecessity of strategic and long-term thinking. By presenting an analysis tojustify maintenance studying system behavior, this paper exemplifies thecontribution of the combined approach of a system dynamics maintenanceperformance model and multi-objective optimization. The paper reveals howinsights from the investigation, of the near optimal Pareto-front solutions inthe objective space, can be drawn using visualization of performance ofselected parameters. According to our analysis, there is no return back to thesingle use of system dynamics; the contribution to the analysis of exploringsystem behavior, from applying multi-objective optimization, is extensive.However, for the practical application, the combined approach is not areplacement – but a complement. Where the interpretation of the visualizedPareto-fronts strongly benefits from the understanding of the model dynamics, inwhich important nonlinearities and delays can be revealed, and thus facilitateon the selected strategical path for implementation.

Place, publisher, year, edition, pages
Curran Associates, Inc., 2017
Keyword
maintenance performance, strategic development, system dynamics, simulation, multi-objective optimization
National Category
Engineering and Technology
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-14707 (URN)9781510851078 (ISBN)
Conference
35th International Conference of the System Dynamics Society, Cambridge, Massachusetts, USA, July 16-20, 2017
Available from: 2018-02-01 Created: 2018-02-01 Last updated: 2018-02-19
Goienetxea Uriarte, A., Ng, A. H. C., Urenda Moris, M. & Jägstam, M. (2017). Lean, Simulation and Optimization: A maturity model. In: Proceedings of the International Conference on Industrial Engineering and Engineering Management, IEEM2017: . Paper presented at 2017 International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, December 10-13, 2017 (pp. 1310-1315). IEEE.
Open this publication in new window or tab >>Lean, Simulation and Optimization: A maturity model
2017 (English)In: Proceedings of the International Conference on Industrial Engineering and Engineering Management, IEEM2017, IEEE, 2017, p. 1310-1315Conference paper, Published paper (Refereed)
Abstract [en]

This article presents a maturity model that can be applied to support organizations in identifying their current state and guiding their further development with regard to lean, simulation and optimization. The paper identifies and describes different maturity levels and offers guidelines that explain how organizations can grow from lower to higher levels of maturity. In addition, it attempts to provide the starting point for organizations that have applied lean or are willing to implement it and which may also be considering taking decisions in a more efficient way via simulation and optimization.

Place, publisher, year, edition, pages
IEEE, 2017
Series
International Conference on Industrial Engineering and Engineering Management, E-ISSN 2157-362X
Keyword
Decision-making, lean, maturity model, optimization, organizational performance, simulation
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:his:diva-14687 (URN)10.1109/IEEM.2017.8290105 (DOI)978-1-5386-0948-4 (ISBN)978-1-5386-0947-7 (ISBN)978-1-5386-0949-1 (ISBN)
Conference
2017 International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, December 10-13, 2017
Available from: 2018-01-25 Created: 2018-01-25 Last updated: 2018-02-22
Ng, A. H. C., Shaaban, S. & Bernedixen, J. (2017). Studying unbalanced workload and buffer allocation of production systems using multi-objective optimisation. International Journal of Production Research, 55(24), 7435-7451.
Open this publication in new window or tab >>Studying unbalanced workload and buffer allocation of production systems using multi-objective optimisation
2017 (English)In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 55, no 24, p. 7435-7451Article in journal (Refereed) Published
Abstract [en]

Numerous studies have investigated the effects of unbalanced service times and inter-station buffer sizes on the efficiency of discrete part, unpaced production lines. There are two main disadvantages of many of these studies: (1) only some predetermined degree of imbalance and patterns of imbalance have been evaluated against the perfectly balanced configuration, making it hard to form a general conclusion on these factors; (2) only a single objective has been set as the target, which neglects the fact that different patterns of imbalance may outperform with respect to different performance measures. Therefore, the aim of this study is to introduce a new approach to investigate the performance of unpaced production lines by using multiple-objective optimisation. It has been found by equipping multi-objective optimisation with an efficient, equality constraints handling technique, both the optimal pattern and degree of imbalance, as well as the optimal relationship among these factors and the performance measures of a production system can be sought and analysed with some single optimisation runs. The results have illustrated that some very interesting relationships among the key performance measures studied, including system throughput, work-in-process and average buffer level, could only be observed within a truly multi-objective optimisation context. While these results may not be generalised to apply to any production lines, the genericity of the proposed simulation-based approach is believed to be applicable to study any real-world, complex production lines.

Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-14730 (URN)10.1080/00207543.2017.1362121 (DOI)000423135100011 ()
Available from: 2018-02-08 Created: 2018-02-08 Last updated: 2018-02-09
Siegmund, F., Ng, A. H. C. & Deb, K. (2016). A Ranking and Selection Strategy for Preference-based Evolutionary Multi-objective Optimization of Variable-Noise Problems. In: 2016 IEEE Congress on Evolutionary Computation (CEC): . Paper presented at 2016 IEEE Congress on Evolutionary Computation (IEEE CEC) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCC) 2016, 24-29 July 2016, Vancouver, Canada (pp. 3035-3044). IEEE conference proceedings.
Open this publication in new window or tab >>A Ranking and Selection Strategy for Preference-based Evolutionary Multi-objective Optimization of Variable-Noise Problems
2016 (English)In: 2016 IEEE Congress on Evolutionary Computation (CEC), IEEE conference proceedings, 2016, p. 3035-3044Conference paper, Published paper (Refereed)
Abstract [en]

In simulation-based Evolutionary Multi-objective Optimization the number of simulation runs is very limited, since the complex simulation models require long execution times. With the help of preference information, the optimization result can be improved by guiding the optimization towards relevant areas in the objective space, for example with the R-NSGA-II algorithm [9], which uses a reference point specified by the decision maker. When stochastic systems are simulated, the uncertainty of the objective values might degrade the optimization performance. By sampling the solutions multiple times this uncertainty can be reduced. However, resampling methods reduce the overall number of evaluated solutions which potentially worsens the optimization result. In this article, a Dynamic Resampling strategy is proposed which identifies the solutions closest to the reference point which guides the population of the Evolutionary Algorithm. We apply a single-objective Ranking and Selection resampling algorithm in the selection step of R-NSGA-II, which considers the stochastic reference point distance and its variance to identify the best solutions. We propose and evaluate different ways to integrate the sampling allocation method into the Evolutionary Algorithm. On the one hand, the Dynamic Resampling algorithm is made adaptive to support the EA selection step, and it is customized to be used in the time-constrained optimization scenario. Furthermore, it is controlled by other resampling criteria, in the same way as other hybrid DR algorithms. On the other hand, R-NSGA-II is modified to rely more on the scalar reference point distance as fitness function. The results are evaluated on a benchmark problem with variable noise landscape.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016
Keyword
Evolutionary, multi-objective optimization, preference-based, guided search, reference point, dynamic resampling, budget allocation, ranking and selection, variable noise
National Category
Information Systems Robotics
Research subject
Technology; Natural sciences
Identifiers
urn:nbn:se:his:diva-13161 (URN)10.1109/CEC.2016.7744173 (DOI)000390749103029 ()2-s2.0-85008255213 (Scopus ID)978-1-5090-0623-6 (ISBN)978-1-5090-0624-3 (ISBN)978-1-5090-0622-9 (ISBN)
Conference
2016 IEEE Congress on Evolutionary Computation (IEEE CEC) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCC) 2016, 24-29 July 2016, Vancouver, Canada
Funder
Knowledge Foundation
Available from: 2016-11-30 Created: 2016-11-30 Last updated: 2018-01-13Bibliographically approved
Pehrsson, L., Ng, A. H. C. & Bernedixen, J. (2016). Automatic identification of constraints and improvement actions in production systems using multi-objective optimization and post-optimality analysis. Journal of manufacturing systems, 39, 24-37.
Open this publication in new window or tab >>Automatic identification of constraints and improvement actions in production systems using multi-objective optimization and post-optimality analysis
2016 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 39, p. 24-37Article in journal (Refereed) Published
Abstract [en]

Manufacturing companies are operating in a severely competitive global market, which renders an urgent need for them to explore new methods to enhance the performance of their production systems in order to retain their competitiveness. Regarding the performance of a production system, it is not sufficient simply to detect which operations to improve, but it is imperative to pinpoint the right actions in the right order to avoid sub-optimizations and wastes in time and expense. Therefore, a more accurate and efficient method for supporting system improvement decisions is greatly needed in manufacturing systems management. Based on research in combining simulation-based multi-objective optimization and post-optimality analysis methods for production systems design and analysis, a novel method for the automatic identification of bottlenecks and improvement actions, so-called Simulation-based Constraint Identification (SCI), is proposed in this paper. The essence of the SCI method is the application of simulation-based multi-objective optimization with the conflicting objectives to maximize the throughput and minimize the number of required improvement actions simultaneously. By using post-optimality analysis to process the generated optimization dataset, the exact improvement actions needed to attain a certain level of performance of the production line are automatically put into a rank order. In other words, when compared to other existing approaches in bottleneck detection, the key novelty of combining multi-objective optimization and post-optimality analysis is to make SCI capable of accurately identifying a rank order for the required levels of improvement for a large number of system parameters which impede the performance of the entire system, in a single optimization run. At the same time, since SCI is basically built a top a simulation-based optimization approach, it is capable of handling large-scale, real-world system models with complicated process characteristics. Apart from introducing such a method, this paper provides some detailed validation results from applying SCI both in hypothetical examples that can easily be replicated as well as a complex, real-world industrial improvement project. The promising results compared to other existing bottleneck detection methods have demonstrated that SCI can provide valuable higher-level information to support confident decision-making in production systems improvement.

Place, publisher, year, edition, pages
Elsevier, 2016
Keyword
Multi-objective optimization, Simulation, Production system, SCI
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-12020 (URN)10.1016/j.jmsy.2016.02.001 (DOI)000376694200003 ()
Available from: 2016-03-08 Created: 2016-03-08 Last updated: 2017-11-30Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0111-1776

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