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Dudas, Catarina
Publications (10 of 14) Show all publications
Karlsson, I., Ng, A. H. C., Aslam, T. & Dudas, C. (2014). An Interactive, Cloud-Based Simulation Optimization System for Knowledge Discovery and Decision Support In Manufacturing. In: Proceedings of the sixth Swedish Production Symposium, 2014: . Paper presented at The sixth Swedish Production Symposium, 2014, September 16-18, Gothenburg.
Open this publication in new window or tab >>An Interactive, Cloud-Based Simulation Optimization System for Knowledge Discovery and Decision Support In Manufacturing
2014 (English)In: Proceedings of the sixth Swedish Production Symposium, 2014, 2014Conference paper, Published paper (Refereed)
Abstract [en]

Designing or improving a manufacturing system involves a series of complex decisions over time to satisfy the strategic objectives of the company. To select the optimal parameters of the system entities so as to achieve the desired overall performance of the system is a very complex task that has been proven to be difficult, even for a seasoned decision maker. One of the major barriers for more efficient decision making in manufacturing is that whilst there is in principle abundant data from various levels of the factory, these data need to be organized and transferred into knowledge suitable for decision-making support. The integration of decision-making support and knowledge management has been identified to be more and more important in both scientific research and from industrial companies. The concept of deciphering knowledge from multi-objective optimization was first proposed by Deb with the term innovization (innovation via optimization). By integrating the concept of innovization with simulation, a new set of powerful tools for manufacturing systems analysis, in order to support optimal decision making in design and improvement activities, is emerged. This method is so-called Simulation-based Innovization (SBI), which has been proven to produce promising results in our previous application studies. Nevertheless, to promote the wider use of such a new method requires the development of an integrated software toolset. The goal of this paper is therefore to outline a Cloud-computing based system architecture for implementing such a SBI-based Interactive Decision Support System.

National Category
Engineering and Technology
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-10385 (URN)
Conference
The sixth Swedish Production Symposium, 2014, September 16-18, Gothenburg
Available from: 2014-12-12 Created: 2014-12-12 Last updated: 2018-03-29Bibliographically approved
Dudas, C., Ng, A. H. .., Pehrsson, L. & Boström, H. (2014). Integration of data mining and multi-objective optimisation for decision support in production system development. International journal of computer integrated manufacturing (Print), 27(9), 824-839
Open this publication in new window or tab >>Integration of data mining and multi-objective optimisation for decision support in production system development
2014 (English)In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 27, no 9, p. 824-839Article 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.

Place, publisher, year, edition, pages
Taylor & Francis, 2014
Keywords
Data Mining, Multi-Objective Optimisation, Decision Support, Production Systems
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-10058 (URN)10.1080/0951192X.2013.834481 (DOI)000337245200002 ()2-s2.0-84901449346 (Scopus ID)
Available from: 2014-10-03 Created: 2014-10-03 Last updated: 2018-03-29Bibliographically approved
Ng, A. H. C., Dudas, C., Boström, H. & Kalyanmoy, D. (2013). Interleaving Innovization with Evolutionary Multi-Objective Optimization in Production System Simulation for Faster ConvergenceOptimization. In: Giuseppe Nicosia, Panos Pardalos (Ed.), Learning and Intelligent Optimization: 7th International Conference, LION 7, Catania, Italy, January 7-11, 2013, Revised Selected Papers (pp. 1-18). Berlin, Heidelberg: Springer Berlin/Heidelberg
Open this publication in new window or tab >>Interleaving Innovization with Evolutionary Multi-Objective Optimization in Production System Simulation for Faster ConvergenceOptimization
2013 (English)In: Learning and Intelligent Optimization: 7th International Conference, LION 7, Catania, Italy, January 7-11, 2013, Revised Selected Papers / [ed] Giuseppe Nicosia, Panos Pardalos, Berlin, Heidelberg: Springer Berlin/Heidelberg, 2013, p. 1-18Chapter in book (Refereed)
Abstract [en]

This paper introduces a novel methodology for the optimization, analysis and decision support in production systems engineering. The methodology is based on the innovization procedure, originally introduced to unveil new and innovative design principles in engineering design problems. The innovization 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 techniques, a new set of powerful tools can be developed for general systems analysis. The uniqueness of the approach introduced in this paper lies in that decision rules extracted from the multi-objective optimization using data mining are used to modify the original optimization. Hence, 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 multi-objective optimization and data mining process. In this paper, such an interleaved approach is illustrated through a set of experiments carried out on a simulation model developed for a real-world production system analysis problem.

Place, publisher, year, edition, pages
Berlin, Heidelberg: Springer Berlin/Heidelberg, 2013
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 7997
Keywords
Innovization, Multi-Objective Optimization, Data Mining, Production Systems
National Category
Computer Sciences
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-10059 (URN)10.1007/978-3-642-44973-4_1 (DOI)2-s2.0-84890892997 (Scopus ID)978-3-642-44972-7 (ISBN)978-3-642-44973-4 (ISBN)
Available from: 2014-10-03 Created: 2014-10-03 Last updated: 2019-05-27Bibliographically approved
Ng, A. H. C., Dudas, C., Pehrsson, L. & Deb, K. (2012). Knowledge Discovery in Production simulation By Interleaving Multi-Objective Optimization and Data Mining. In: Proceedings of the SPS12 conference 2012. Paper presented at The 5th International Swedish Production Symposium 6th – 8th of November 2012 Linköping, Sweden (pp. 461-471). The Swedish Production Academy
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, p. 461-471Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
The Swedish Production Academy, 2012
Keywords
Production System Simulation, Multi-objective Optimization, Data Mining, Innovization
National Category
Computer Sciences
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-7161 (URN)978-91-7519-752-4 (ISBN)
Conference
The 5th International Swedish Production Symposium 6th – 8th of November 2012 Linköping, Sweden
Available from: 2013-02-08 Created: 2013-02-07 Last updated: 2018-01-11Bibliographically approved
Dudas, C., Frantzén, M. & Ng, A. H. .. (2011). A synergy of multi-objective optimization and data mining for the analysis of a flexible flow shop. Paper presented at 20th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM), California State Univ, Oakland, CA, 2010. Robotics and Computer-Integrated Manufacturing, 27(4), 687-695
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, p. 687-695Article 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
Keywords
Data mining, Decision trees, Post-optimality analysis, Simulation-based optimization
National Category
Engineering and Technology
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-4860 (URN)10.1016/j.rcim.2010.12.005 (DOI)000291458900005 ()2-s2.0-79955664950 (Scopus ID)
Conference
20th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM), California State Univ, Oakland, CA, 2010
Available from: 2011-05-02 Created: 2011-05-02 Last updated: 2017-12-11Bibliographically approved
Dudas, C., Hedenstierna, P. & Ng, A. H. C. (2011). Simulation-based innovization for manufacturing systems analysis using data mining and visual analytics. In: Proceedings of the 4th Swedish Production Symposium: . Paper presented at The 4th Swedish Production Symposium 3-5 May 2011, Lund (pp. 374-382).
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, p. 374-382Conference paper, Published paper (Refereed)
National Category
Engineering and Technology
Identifiers
urn:nbn:se:his:diva-7323 (URN)
Conference
The 4th Swedish Production Symposium 3-5 May 2011, Lund
Available from: 2013-02-26 Created: 2013-02-26 Last updated: 2017-11-27Bibliographically approved
Ng, A. H. C., Dudas, C., Nießen, J. & Deb, K. (2011). Simulation-Based Innovization Using Data Mining for Production Systems Analysis. In: Lihui Wang, Amos H. C. Ng, Kalyanmoy Deb (Ed.), Multi-objective Evolutionary Optimisation for Product Design and Manufacturing: (pp. 401-429). Springer London
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, Springer London, 2011, p. 401-429Chapter 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
Springer London, 2011
National Category
Engineering and Technology
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-5839 (URN)10.1007/978-0-85729-652-8_15 (DOI)978-0-85729-617-7 (ISBN)978-0-85729-652-8 (ISBN)
Available from: 2012-05-03 Created: 2012-05-03 Last updated: 2017-11-27Bibliographically approved
Dudas, C., Aslam, T. & Ng, A. (2010). Frequent Itemset Mining to Generate Initial Solutions for Simulation-Based Optimization of Warehouse Product Placement. In: SCMIS 2010: Proceedings of the 8th International Conference on Supply Chain Management and Information Systems (Conference Theme: Logistics Systems and Engineering) 6th-8th October 2010 Hong Kong, China. Paper presented at 8th International Conference on Supply Chain Management and Information Systems (Conference Theme: Logistics Systems and Engineering) 6th-8th October 2010 Hong Kong, China. Hong Kong: The Hong Kong Polytechnic University
Open this publication in new window or tab >>Frequent Itemset Mining to Generate Initial Solutions for Simulation-Based Optimization of Warehouse Product Placement
2010 (English)In: SCMIS 2010: Proceedings of the 8th International Conference on Supply Chain Management and Information Systems (Conference Theme: Logistics Systems and Engineering) 6th-8th October 2010 Hong Kong, China, Hong Kong: The Hong Kong Polytechnic University , 2010Conference paper, Published paper (Refereed)
Abstract [en]

Warehouses are obliged to optimize their operations with regard to  multiple  objectives,  such  as  maximizing  effective  use  space, equipment,  labor,  maximize  accessibility  of  products,  maximize amount of processed orders and all this should be achieved whilst minimizing  order  processing  times,  distance  traveled,  broken promises, errors and not to forget the operational cost. A product placement problem for a warehouse is in focus of this study and the main goal is to decrease the picking time for each pick run in order to gain higher efficiency.  To achieve this, a simulation model is built as a representation of the warehouse. As the complexity and the size of the number  of input   variable   grow   it   is   essential   to   use   simulation-based optimization in order to receive a satisfying result. A set of initial solutions  for  the  simulation-based  optimization  is  needed;  since the  number  of  products  to  place  in  the  warehouse  is  huge  this solution ought to be intelligent. This paper describes a technique for  generating  such  a  set  of  solutions  through  searching  for frequent itemsets in the transaction  database. It is  believed that frequent products usually picked simultaneously should be stored closed together.

Place, publisher, year, edition, pages
Hong Kong: The Hong Kong Polytechnic University, 2010
Keywords
Warehouse Planning, Frequent Items, Simulation-Based Optimization
National Category
Engineering and Technology
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-4595 (URN)978-962-367-697-7 (ISBN)978-962-367-696-0 (ISBN)
Conference
8th International Conference on Supply Chain Management and Information Systems (Conference Theme: Logistics Systems and Engineering) 6th-8th October 2010 Hong Kong, China
Available from: 2011-01-20 Created: 2011-01-20 Last updated: 2017-11-27Bibliographically approved
Dudas, C., Frantzén, M. & Ng, A. (2010). Simulation-Based Innovization for the Analysis of a Machining Line. In: 20th International Conference on Flexible Automation and Intelligent Manufacturing 2010 (FAIM 2010): Volume 1 of 2. Paper presented at 20th International Conference on Flexible Automation and Intelligent Manufacturing 2010 (FAIM 2010), Oakland, California, USA, 12-14 July 2010 (pp. 959-966). Curran Associates, Inc.
Open this publication in new window or tab >>Simulation-Based Innovization for the Analysis of a Machining Line
2010 (English)In: 20th International Conference on Flexible Automation and Intelligent Manufacturing 2010 (FAIM 2010): Volume 1 of 2, Curran Associates, Inc., 2010, p. 959-966Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Curran Associates, Inc., 2010
National Category
Engineering and Technology
Identifiers
urn:nbn:se:his:diva-7135 (URN)978-1-61782-714-3 (ISBN)
Conference
20th International Conference on Flexible Automation and Intelligent Manufacturing 2010 (FAIM 2010), Oakland, California, USA, 12-14 July 2010
Available from: 2013-02-07 Created: 2013-02-07 Last updated: 2017-11-27Bibliographically approved
Dudas, C., Ng, A. & Boström, H. (2009). Information Extraction from Solution Set of Simulation-based Multi-objective Optimisation using Data Mining. In: D. B. Das, V. Nassehi & L. Deka (Ed.), Proceedings of Industrial Simulation Conference 2009: . Paper presented at 7th International Industrial Simulation Conference 2009, ISC'09, June 1-3, 2009, Loughborough, United Kingdom (pp. 65-69). EUROSIS-ETI
Open this publication in new window or tab >>Information Extraction from Solution Set of Simulation-based Multi-objective Optimisation using Data Mining
2009 (English)In: Proceedings of Industrial Simulation Conference 2009 / [ed] D. B. Das, V. Nassehi & L. Deka, EUROSIS-ETI , 2009, p. 65-69Conference 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.

Place, publisher, year, edition, pages
EUROSIS-ETI, 2009
Keywords
Output analysis, Data mining, Information extraction
National Category
Computer and Information Sciences
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-3301 (URN)000280184200011 ()2-s2.0-84898467726 (Scopus ID)9789077381489 (ISBN)
Conference
7th International Industrial Simulation Conference 2009, ISC'09, June 1-3, 2009, Loughborough, United Kingdom
Available from: 2009-07-10 Created: 2009-07-10 Last updated: 2018-01-13Bibliographically approved
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