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Linnéusson, G., Ng, A. H. C. & Aslam, T. (2018). A hybrid simulation-based optimization framework for supporting strategic maintenance to improve production performance. European Journal of Operational Research
Open this publication in new window or tab >>A hybrid simulation-based optimization framework for supporting strategic maintenance to improve production performance
2018 (English)In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860Article in journal (Refereed) Submitted
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15064 (URN)
Available from: 2018-04-16 Created: 2018-04-16 Last updated: 2018-04-16
Karlsson, I., Bernedixen, J., Ng, A. H. C. & Pehrsson, L. (2018). Combining augmented reality and simulation-based optimization for decision support in manufacturing. In: W. K. V. Chan, A. D’Ambrogio, G. Zacharewicz, N. Mustafee, G. Wainer, and E. Page (Ed.), Proceedings of the 2017 Winter Simulation Conference: . Paper presented at 2017 Winter Simulation Conference, WSC 2017 (pp. 3988-3999). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Combining augmented reality and simulation-based optimization for decision support in manufacturing
2018 (English)In: Proceedings of the 2017 Winter Simulation Conference / [ed] W. K. V. Chan, A. D’Ambrogio, G. Zacharewicz, N. Mustafee, G. Wainer, and E. Page, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 3988-3999Conference paper, Published paper (Refereed)
Abstract [en]

Although the idea of using Augmented Reality and simulation within manufacturing is not a new one, the improvement of hardware enhances the emergence of new areas. For manufacturing organizations, simulation is an important tool used to analyze and understand their manufacturing systems; however, simulation models can be complex. Nonetheless, using Augmented Reality to display the simulation results and analysis can increase the understanding of the model and the modeled system. This paper introduces a decision support system, IDSS-AR, which uses simulation and Augmented Reality to show a simulation model in 3D. The decision support system uses Microsoft HoloLens, which is a head-worn hardware for Augmented Reality. A prototype of IDSS-AR has been evaluated with a simulation model depicting a real manufacturing system on which a bottleneck detection method has been applied. The bottleneck information is shown on the simulation model, increasing the possibility of realizing interactions between the bottlenecks. © 2017 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Series
Winter Simulation Conference. Proceedings, ISSN 0891-7736
Keyword
Artificial intelligence, Augmented reality, Automobile drivers, Decision support systems, Hardware, Optimization, Bottleneck detection, Decision supports, Manufacturing IS, Manufacturing organizations, MicroSoft, Simulation model, Simulation-based optimizations, Quality control
National Category
Computer Sciences Computer Systems Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15109 (URN)10.1109/WSC.2017.8248108 (DOI)2-s2.0-85044511682 (Scopus ID)9781538634288 (ISBN)
Conference
2017 Winter Simulation Conference, WSC 2017
Available from: 2018-04-30 Created: 2018-04-30 Last updated: 2018-05-14
Linnéusson, G., Ng, A. H. C. & Aslam, T. (2018). Quantitative analysis of a conceptual system dynamics maintenance performance model using multi-objective optimisation. Journal of Simulation, 12(2), 171-189
Open this publication in new window or tab >>Quantitative analysis of a conceptual system dynamics maintenance performance model using multi-objective optimisation
2018 (English)In: Journal of Simulation, ISSN 1747-7778, E-ISSN 1747-7786, Vol. 12, no 2, p. 171-189Article in journal (Refereed) Published
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15063 (URN)10.1080/17477778.2018.1467849 (DOI)
Available from: 2018-04-16 Created: 2018-04-16 Last updated: 2018-05-16Bibliographically approved
Amouzgar, K., Bandaru, S. & Ng, A. H. C. (2018). Radial basis functions with a priori bias as surrogate models: A comparative study. Engineering applications of artificial intelligence, 71, 28-44
Open this publication in new window or tab >>Radial basis functions with a priori bias as surrogate models: A comparative study
2018 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 71, p. 28-44Article in journal (Refereed) Published
Abstract [en]

Radial basis functions are augmented with a posteriori bias in order to perform robustly when used as metamodels. Recently, it has been proposed that the bias can simply be set a priori by using the normal equation, i.e., the bias becomes the corresponding regression model. In this study, we demonstrate the performance of the suggested approach (RBFpri) with four other well-known metamodeling methods; Kriging, support vector regression, neural network and multivariate adaptive regression. The performance of the five methods is investigated by a comparative study, using 19 mathematical test functions, with five different degrees of dimensionality and sampling size for each function. The performance is evaluated by root mean squared error representing the accuracy, rank error representing the suitability of metamodels when coupled with evolutionary optimization algorithms, training time representing the efficiency and variation of root mean squared error representing the robustness. Furthermore, a rigorous statistical analysis of performance metrics is performed. The results show that the proposed radial basis function with a priori bias achieved the best performance in most of the experiments in terms of all three metrics. When considering the statistical analysis results, the proposed approach again behaved the best, while Kriging was relatively as accurate and support vector regression was almost as fast as RBFpri. The proposed RBF is proven to be the most suitable method in predicting the ranking among pairs of solutions utilized in evolutionary algorithms. Finally, the comparison study is carried out on a real-world engineering optimization problem. © 2018 Elsevier Ltd

Place, publisher, year, edition, pages
Elsevier, 2018
Keyword
Kriging, Metamodeling, Multivariate adaptive regression splines, Neural networks, Radial basis function, Support vector regression, Surrogate models, Errors, Evolutionary algorithms, Functions, Heat conduction, Image segmentation, Interpolation, Mean square error, Optimization, Regression analysis, Statistical methods, Radial basis functions, Support vector regression (SVR), Surrogate model, Radial basis function networks
National Category
Mechanical Engineering
Research subject
Mechanics of Materials; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-14999 (URN)10.1016/j.engappai.2018.02.006 (DOI)2-s2.0-85042877194 (Scopus ID)
Available from: 2018-04-01 Created: 2018-04-03 Last updated: 2018-05-14
Linnéusson, G., Ng, A. H. C. & Aslam, T. (2018). Relating strategic time horizons and proactiveness in equipment maintenance: a simulation-based optimization study. In: : . Paper presented at 51st CIRP Conference on Manufacturing Systems.
Open this publication in new window or tab >>Relating strategic time horizons and proactiveness in equipment maintenance: a simulation-based optimization study
2018 (English)Conference paper, Published paper (Refereed)
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15066 (URN)
Conference
51st CIRP Conference on Manufacturing Systems
Available from: 2018-04-16 Created: 2018-04-16 Last updated: 2018-04-16
Linnéusson, G., Ng, A. H. C. & Aslam, T. (2018). Towards strategic development of maintenance and its effects on production performance by using system dynamics in the automotive industry. International Journal of Production Economics, 200, 151-169
Open this publication in new window or tab >>Towards strategic development of maintenance and its effects on production performance by using system dynamics in the automotive industry
2018 (English)In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 200, p. 151-169Article in journal (Refereed) Published
Abstract [en]

Managing maintenance within an economical short-termism framework, without considering the consequential long-term cost effect, is very common in industry. This research presents a novel conceptual system dynamics model for the study of the dynamic behaviors of maintenance performance and costs, which aims to illuminate insights for the support of the long-term, strategic development of manufacturing maintenance. By novel, we claim the model promotes a system's view of maintenance costs that include its dynamic consequential costs as the combined result of several interacting maintenance levels throughout the constituent feedback structures. These range from the applied combination of maintenance methodologies to the resulting proactiveness in production, which is based on the rate of continuous improvements arising from the root cause analyses of breakdowns. The purpose of using system dynamics is to support the investigations of the causal relationships between strategic initiatives and performance results, and to enable analyses that take into consideration the time delays between different actions, in order to support the sound formulation of policies to develop maintenance and production performances. The model construction and validation process has been supported by two large maintenance organizations operating in the Swedish automotive industry. Experimental results show that intended changes can have both short and long-term consequences, and that obvious and hidden dynamic behavioral effects, which have not been reported in the literature previously, may be in the system. We believe the model can help to illuminate the holistic value of maintenance on the one hand and support its strategic development as well as the organizational transformation into proactiveness on the other.

Keyword
Maintenance performance, Strategic development, System dynamics, Simulation
National Category
Engineering and Technology Reliability and Maintenance Other Mechanical Engineering Mechanical Engineering
Research subject
INF201 Virtual Production Development; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15002 (URN)10.1016/j.ijpe.2018.03.024 (DOI)
Projects
IPSI
Available from: 2018-04-03 Created: 2018-04-03 Last updated: 2018-04-13Bibliographically approved
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; Production and Automation Engineering
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: 2018-03-28Bibliographically 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; Production and Automation Engineering
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: 2018-03-28Bibliographically 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 ()2-s2.0-85032745554 (Scopus ID)
Available from: 2017-11-15 Created: 2017-11-15 Last updated: 2018-05-07Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0111-1776

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