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  • 51.
    De Vin, Leo
    et al.
    University of Skövde, School of Technology and Society.
    Ng, Amos H. C.
    University of Skövde, School of Technology and Society.
    Sundberg, Martin
    University of Skövde, School of Technology and Society.
    Moore, Philip R.
    De Montfort Univ, Mechatron Res Ctr, Leicester LE1 9BH, Leics, England.
    Pu, Junsheng
    De Montfort Univ, Mechatron Res Ctr, Leicester LE1 9BH, Leics, England.
    Wong, Bill C.-B.
    De Montfort Univ, Mechatron Res Ctr, Leicester LE1 9BH, Leics, England.
    Information fusion for decision support in manufacturing: studies from the defense sector2008In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 35, no 9-10, p. 908-915Article in journal (Other academic)
    Abstract [en]

    Information fusion, the synergistic combination of information from multiple sources, is an established research area within the defense sector. In manufacturing however, it is less well-established, with the exception of sensor/data fusion for automatic decision making. The paper briefly discusses some military specific models and methods for information fusion; analogies with manufacturing as well as a more generalized terminology are presented. “Manufacturing” is an application scenario within a Swedish information fusion research program that studies information fusion from databases, sensors and simulations with (currently) a focus on support for human decision making. An area of particular interest is that of advanced applications of virtual manufacturing such as synthetic environments, a form of hardware in the loop simulation that can deliver services such as service and maintenance at remote locations. In this area, the manufacturing industry can benefit from ongoing work in the defense sector related to verification, validation and accreditation of simulation models.

  • 52.
    De Vin, Leo
    et al.
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Jägstam, Mats
    University of Skövde, School of Technology and Society.
    Karlsson, Thomas
    University of Skövde, School of Technology and Society.
    Virtual Manufacturing: Good Practice, Pitfalls and Advanced Applications2006In: SME Technical Paper, ISSN 0081-1653, p. TP06PUB41-Article in journal (Refereed)
    Abstract [en]

    This paper describes manufacturing (resource) simulation with a focus on discrete event simulation and computer-aided robotics. Some generic good practices, problems and pitfalls in the use of simulation are described. Various advanced applications of manufacturing simulation are described and elucidated on the hand of a system for simulation-based service and maintenance. The paper also describes briefly how simulation-based decision support and information fusion are related, and how this can result in synergistic research across these areas

  • 53.
    De Vin, Leo
    et al.
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Oscarsson, Jan
    University of Skövde, School of Technology and Society.
    Andler, Sten F.
    University of Skövde, School of Technology and Society.
    Information Fusion for Simulation Based Decision Support in Manufacturing2006In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 22, no 5-6, p. 429-436Article in journal (Refereed)
    Abstract [en]

    Robust and informed decisions are important for the efficient and effective operation of installed production facilities. The paper discusses Information fusion (IF) including a generic model for IF, and situations for decision-making. The paper also discusses current and future use of manufacturing resource simulation for design/configuration, operational planning and scheduling, and service and maintenance of manufacturing systems. Many of these applications use IF in some way, as is explained in more detail for simulation based service and maintenance. An extension of the generic model for IF is presented which incorporates modeling and simulation, and active databases as used in a simulation based service and maintenance system at the authors’ laboratory

  • 54.
    De Vin, Leo
    et al.
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Oskarsson, Jan
    University of Skövde, School of Technology and Society.
    Simulation-based decision support for manufacturing system life cycle management2004In: Journal of Advanced Manufacturing Systems, ISSN 0219-6867, Vol. 3, no 2, p. 115-128Article in journal (Refereed)
    Abstract [en]

    Previous research has highlighted the role of virtual engineering tools in the development of manufacturing machinery systems. Simulation models created for this purpose can potentially be used to provide support for other tasks, such as operational planning and service and maintenance. This requires that the simulation models can be fed with historic data as well as with snapshot data. Furthermore, the models must be able to communicate with other business software. The paper describes how simulation models can be used for operational production planning and for service and maintenance support. Benefits include a better possibility to verify production plans and the possibility to monitor and service manufacturing machinery from remote locations. Furthermore, the expanded and continuously updated models provide a good tool to study the effect of, for instance, planned new product introduction in existing manufacturing systems. The paper also presents directions for future research. One ambition is to add AI tools to the system so as to develop a semi-autonomous system for decision support

  • 55.
    De Vin, Leo
    et al.
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Urenda Moris, Matias
    University of Skövde, School of Technology and Society.
    Jägstam, Mats
    University of Skövde, School of Technology and Society.
    King, P. D.
    Ballester, E.
    Fuentes, P.
    MMII: Engineering studies with a truly European dimension2006In: Innovative teaching and learning in engineering education: Valencia Global Conference, 2006, p. 99-106Conference paper (Refereed)
    Abstract [en]

    The paper describes a new engineering Master's program called MMII (manufacturing management and industrial informatics) that is co-located at universities in Sweden, Spain and the United Kingdom. One reason for developing the program was that the changing manufacturing landscape due to globalisation, increasing complexity of manufacturing systems itself and an increased need to integrate manufacturing systems with corporate information systems forces educators to find solutions that provide industry with engineers who have the right skills. Apart from “hard” skills related to the above-mentioned issues, industry increasingly also requires engineers to have well-developed “soft” skills such as an ability to work in an international environment and willingness to work abroad. A program given at only one location would not provide a truly European dimension and besides, it would draw heavily upon the teaching resources; hence the decision to seek international partners with complementing competences and resources; these were found at universities in Skövde, Valencia and Loughborough. In Loughborough, students read capita selecta from CAE (computer aided engineering) during one semester. In Valencia, they spend a project-based semester on international industrial management. In Skövde, they read virtual manufacturing during one semester and carry out their degree project during the final semester.

  • 56.
    De Vin, Leo
    et al.
    University of Skövde, School of Technology and Society.
    Oscarsson, Jan
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Jägstam, Mats
    University of Skövde, School of Technology and Society.
    Karlsson, Thomas
    University of Skövde, School of Technology and Society.
    Manufacturing simulation: Good practice, pitfalls, and advanced applications2004In: The 21st International Manufacturing Conference: IMC / [ed] Phelan, P., 2004, p. 156-163Conference paper (Refereed)
    Abstract [en]

    The paper describes manufacturing simulation with a focus on discrete event simulation and computer aided robotics. Some generic good practices, problems, and pitfalls in the use of simulation are described. Some advanced applications of manufacturing simulation are described and elucidated on the hand of a system for simulation-based service & maintenance. Simulation-based decision support and information fusion are closely related, and plans for novel synergistic research in these area are presented

  • 57.
    De Vin, Leo
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Solding, P.
    Swerea SWECAST AB.
    Ng, Amos
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Approaches to Energy Efficiency Assessment and Management: A State of the Art Study2009In: Proceedings of the 26th International Manufacturing Conference / [ed] Garret E O'Donnell; Kevin Kelly; Irish Manufacturing Committee., Department of Mechanical and Manufacturing Engineering, Trinity College Dublin , 2009, p. 9-16Conference paper (Refereed)
    Abstract [en]

    Electricity prices have gone up significantly during the last decade, and in some countries, the electricity price is very volatile. There exist a number of policies and regulations to control the electricity market and to stimulate energy efficiency, but these are often not very transparent. Furthermore, indicators for energy efficiency can be misleading or difficult to measure. Although there are a number of approaches to modelling and simulation of energy consumption and energy optimisation, these approaches have some drawbacks such as prohibitive computing times, difficulties to address dynamic situations, and the lack of a holistic view on the manufacturing system. Other problems include the limited availability of data at the appropriate level of aggregation; sometimes the level is too low, in other cases too high. However, recently promising attempts have been made to use Discrete Event Simulation for energy modelling and production planning. It is believed that this, in combination with other ICT tools such as multi-objective optimisation, offers a promising way forward beyond current state of the art.

  • 58.
    Deb, Kalyanmoy
    et al.
    Department of Electrical and Computer Engineering, Michigan State University, USA.
    Siegmund, Florian
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    R-HV: A Metric for Computing Hyper-volume for Reference Point-based EMOs2015In: Swarm, Evolutionary, and Memetic Computing: 5th International Conference, SEMCCO 2014, Bhubaneswar, India, December 18-20, 2014, Revised Selected Papers / [ed] Bijaya Ketan Panigrahi, Ponnuthurai Nagaratnam Suganthan & Swagatam Das, Springer, 2015, p. 98-110Chapter in book (Refereed)
    Abstract [en]

    For evaluating performance of a multi-objective optimizationfor finding the entire efficient front, a number of metrics, such as hypervolume, inverse generational distance, etc. exists. However, for evaluatingan EMO algorithm for finding a subset of the efficient frontier, the existing metrics are inadequate. There does not exist many performancemetrics for evaluating a partial preferred efficient set. In this paper, wesuggest a metric which can be used for such purposes for both attainableand unattainable reference points. Results on a number of two-objectiveproblems reveal its working principle and its importance in assessingdifferent algorithms. The results are promising and encouraging for itsfurther use.

  • 59.
    Dudas, Catarina
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Aslam, Tehseen
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Frequent Itemset Mining to Generate Initial Solutions for Simulation-Based Optimization of Warehouse Product Placement2010In: 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 (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.

  • 60.
    Dudas, Catarina
    et al.
    University of Skövde, School of Technology and Society.
    Frantzén, Marcus
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Simulation-Based Innovization for the Analysis of a Machining Line2010In: 20th International Conference on Flexible Automation and Intelligent Manufacturing 2010 (FAIM 2010): Volume 1 of 2, Curran Associates, Inc., 2010, p. 959-966Conference paper (Refereed)
  • 61.
    Dudas, Catarina
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Frantzén, Marcus
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Ng, Amos H.C.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    A synergy of multi-objective optimization and data mining for the analysis of a flexible flow shop2011In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 27, no 4, p. 687-695Article in journal (Refereed)
    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.

  • 62.
    Dudas, Catarina
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Hedenstierna, Philip
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Simulation-based innovization for manufacturing systems analysis using data mining and visual analytics2011In: Proceedings of the 4th Swedish Production Symposium, 2011, p. 374-382Conference paper (Refereed)
  • 63.
    Dudas, Catarina
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Boström, Henrik
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Information Extraction from Solution Set of Simulation-based Multi-objective Optimisation using Data Mining2009In: Proceedings of Industrial Simulation Conference 2009 / [ed] D. B. Das, V. Nassehi & L. Deka, EUROSIS-ETI , 2009, p. 65-69Conference 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.

  • 64.
    Dudas, Catarina
    et al.
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Boström, Henrik
    University of Skövde, School of Humanities and Informatics.
    Knowledge Extraction in Manufacturing using Data Mining Techniques2008In: Proceedings of the Swedish Production Symposium 2008, Stockholm, Sweden, November 18-20, 2008, 2008, p. 8 sidor-Conference paper (Refereed)
    Abstract [en]

    Nowadays many production companies collect and store production and process data in large databases. Unfortunately the data is rarely used in the most value generating way, i.e.,  finding  patterns  of  inconsistencies  and  relationships  between  process  settings  and quality  outcome.  This  paper  addresses  the  benefits  of  using  data  mining  techniques  in manufacturing  applications.  Two  different  applications  are  being  laid  out  but  the  used technique  and  software  is  the  same  in  both  cases.  The  first  case  deals  with  how  data mining  can  be  used  to  discover  the  affect  of  process  timing  and  settings  on  the  quality outcome in the casting industry. The result of a multi objective optimization of a camshaft process  is  being  used  as  the  second  case.  This  study  focuses  on  finding  the  most appropriate dispatching rule settings in the buffers on the line.  The  use  of  data  mining  techniques  in  these  two  cases  generated  previously  unknown knowledge. For example, in order to maximize throughput in the camshaft production, let the dispatching rule for the most severe bottleneck be of type Shortest Processing Time (SPT) and for the second bottleneck use any but Most Work Remaining (MWKR).

  • 65.
    Dudas, Catarina
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. University of Skövde.
    Boström, Henrik
    Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.
    Post-analysis of multi-objective optimization solutions using decision trees2015In: Intelligent Data Analysis, ISSN 1088-467X, E-ISSN 1571-4128, Vol. 19, no 2, p. 259-278Article in journal (Refereed)
    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.

  • 66.
    Dudas, Catarina
    et al.
    University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H.C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Pehrsson, Leif
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Boström, Henrik
    Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden.
    Integration of data mining and multi-objective optimisation for decision support in production system development2014In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 27, no 9, p. 824-839Article in journal (Refereed)
    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.

  • 67.
    Fathi, Masood
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Nourmohammadi, Amir
    Department of Industrial & Systems Engineering, Tarbiat Modares University, Tehran, Iran.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Assembly Line Balancing Type-E with Technological Requirement: A Mathematical Model2018In: Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden / [ed] Peter Thorvald, Keith Case, Amsterdam: IOS Press, 2018, p. 183-188Conference paper (Refereed)
    Abstract [en]

    This study is motivated by a real-world assembly line in an automotive manufacturing company and it addresses the simple assembly line balancing problem type-E (SALBPE). The SALBPE aims to maximize the balance efficiency (BE) through determining the best combinations of cycle time and station number. To cope with the problem, a mixed integer nonlinear programming (MINLP) model is proposed. The MINLP model differs from the existing ALBPE models as it includes the technological requirements of assembly tasks and optimizes the variation of workload beside the BE. The validity of the proposed model is tested by solving the real-world case study and a set of benchmark problems.

  • 68.
    Fathi, Masood
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Nourmohammadi, Amir
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    An optimization model for balancing assembly lines with stochastic task times and zoning constraints2019In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 32537-32550, article id 8663269Article in journal (Refereed)
    Abstract [en]

    This study aims to bridge the gap between theory and practice by addressing a real-world assembly line balancing problem (ALBP) where task times are stochastic and there are zoning constraints in addition to the commonly known ALBP constraints. A mixed integer programming (MIP) model is proposed for each of the straight and U-shaped assembly line configurations. The primary objective in both cases is to minimize the number of stations; minimizing the maximum of stations’ mean time and the stations’ time variance are considered secondary objectives. Four different scenarios are discussed for each model, with differences in the objective function. The models are validated by solving a real case taken from an automobile manufacturing company and some standard test problems available in the literature. The results indicate that both models are able to provide optimum solutions for problems of different sizes. The technique for order preference by similarity to ideal solution (TOPSIS) is used to create reliable comparisons of the different scenarios and valid analysis of the results. Finally, some insights regarding the selection of straight and U-shaped layouts are provided.

  • 69.
    Fathi, Masood
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Nourmohammadi, Amir
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Eskandari, Hamidreza
    Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.
    An improved genetic algorithm with variable neighborhood search to solve the assembly line balancing problem2019In: Engineering computations, ISSN 0264-4401, E-ISSN 1758-7077Article in journal (Refereed)
    Abstract [en]
    • Purpose – This study aims to propose an efficient optimization algorithm to solve the assembly line balancing problem (ALBP). The ALBP arises in high-volume, lean production systems when decision makers aim to design an efficient assembly line while satisfying a set of constraints.
    • Design/methodology/approach – An improved genetic algorithm (IGA) is proposed in this study to deal with ALBP in order to optimize the number of stations and the workload smoothness.
    • Findings – To evaluate the performance of the IGA, it is used to solve a set of well-known benchmark problems and a real-life problem faced by an automobile manufacturer. The solutions obtained are compared against two existing algorithms in the literature and the basic genetic algorithm. The comparisons show the high efficiency and effectiveness of the IGA in dealing with ALBPs.
    • Originality/value – The proposed IGA benefits from a novel generation transfer mechanism that improves the diversification capability of the algorithm by allowing population transfer between different generations. In addition, an effective variable neighborhood search is employed in the IGA to enhance its local search capability.
  • 70.
    Frantzén, Marcus
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Holm, Magnus
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Karlsson, V.
    Volvo Group Trucks Operations, Powertrain Production Skövde, Skövde, Sweden.
    Bremert, M.
    Volvo Group Trucks Operations, Powertrain Production Skövde, Skövde, Sweden.
    Dynamic maintenance priority of a real-world machining line2016In: Proceedings of the 7th Swedish Production Symposium, 2016Conference paper (Refereed)
    Abstract [en]

    To support the shop-floor operators, decision support systems (DSS) are becoming more and more vital to the success of manufacturing systems in industry today. In order to get a DSS able to adapt to disturbances in a production system, on-line data are needed to be able to make optimal or near-optimal decisions in real-time (soft real-time). This paper investigates one part of such a system, i.e. how different priorities of maintenance activities (planned and unplanned) affect the productivity of a production system. A discrete-event simulation model has been built for a real-world machining line in order to simulate the decisions made in subject to disturbances. This paper presents a way of prioritizing operators and machines based on multiple criteria such as competence, utilization, distance, bottleneck, and Work-In-Process. An experimental study based on the real-world production system has shown promising results and given insights of how to prioritize the operators in a good way. Another novelty introduced in this paper is the use of simulation-based optimization to generate composite dispatching rules in order to find good tradeoffs when taking a decision of which machine or operator to select.

  • 71.
    Frantzén, Marcus
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    A case study of applying simulation-based optimisation to a real-world scheduling problem2010In: ORbit, ISSN 1601-8893, no 17, p. 4-7Article in journal (Other (popular science, discussion, etc.))
  • 72.
    Frantzén, Marcus
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Production simulation education using rapid modeling and optimization: Successful studies2015In: Proceedings of the 2015 Winter Simulation Conference / [ed] L. Yilmaz, W. K. V. Chan, I. Moon, T. M. K. Roeder, C. Macal, and M. D. Rossetti, Piscataway, NJ: IEEE Press, 2015, p. 3526-3537Conference paper (Refereed)
    Abstract [en]

    A common issue facing many simulation educators is that students usually spend excessive time to struggle with the programming and statistic parts of the simulation courses, and simply very little time to learn running systems analysis. If the students are coming from industry, and not the campus, then the problem becomes even worse. We observed this problem around 2005 and started to develop a new simulation software, a factory conceptual design toolset, partly aimed to address this problem. A new set of educational courses has since then been developed around the software for teaching production systems analysis, with both the campus students and managers/engineers from industry in mind. In this paper, we briefly introduce the software and share our experiences and some representative, successful studies conducted by the students in the past years.

  • 73.
    Frantzén, Marcus
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Ng, Amos H. C.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Moore, Philip
    Computing Sciences and Engineering, De Montfort University Leicester, LE1 9BH, United Kingdom.
    A simulation-based scheduling system for real-time optimization and decision making support2011In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 27, no 4, p. 696-705Article in journal (Refereed)
    Abstract [en]

    This paper presents an industrial application of simulation-based optimization (SBO) in the scheduling and real-time rescheduling of a complex machining line in an automotive manufacturer in Sweden. Apart from generating schedules that are robust and adaptive, the scheduler must be able to carry out rescheduling in real time in order to cope with the system uncertainty effectively. A real-time scheduling system is therefore needed to support not only the work of the production planner but also the operators on the shop floor by re-generating feasible schedules when required. This paper describes such a real-time scheduling system, which is in essence a SBO system integrated with the shop floor database system. The scheduling system, called OPTIMISE scheduling system (OSS), uses real-time data from the production line and sends back expert suggestions directly to the operators through Personal Digital Assistants (PDAs). The user interface helps in generating new schedules and enables the users to easily monitor the production progress through visualization of production status and allows them to forecast and display target performance measures. Initial results from this industrial application have shown that such a novel scheduling system can help both in improving the line throughput efficiently and simultaneously supporting real-time decision making.

  • 74.
    Frantzén, Marcus
    et al.
    University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society.
    Moore, Philip
    University of Skövde, School of Technology and Society.
    A Scheduling System for Real-Time Decision Making Support Using Simulation-Based Optimization2010In: 20th International Conference on Flexible Automation and Intelligent Manufacturing 2010 (FAIM 2010): Volume 2 of 2, Curran Associates, Inc., 2010, p. 980-987Conference paper (Refereed)
  • 75.
    Gandhi, Kanika
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Machine maintenance decision support system: A systematic literature review2018In: Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11 – 13, 2018, Skövde, Sweden / [ed] Peter Thorvald, Keith Case, Amsterdam: IOS Press, 2018, p. 349-354Conference paper (Refereed)
    Abstract [en]

    Growing competition market situations have emerged the requirement of the real-time data, understanding data behaviour, and maintenance actions in the manufacturing system. The future decision-making process in manufacturing needs to be more flexible to adapt to various methods for maintenance decision support systems (MDSS). This paper classifies various application areas of MDSS through a systemic literature review. Specifically, it identifies the relationship between the machine maintenance areas and the processes in which it integrates different tools and techniques to develop MDSS. The accumulated information helps in analyzing trends and shortcomings to concentrate the efforts for future research work. The reviewed papers are selected based on the contents, application tool assessments and clustered by their application areas. Furthermore, it proposes a structure outlined based on the functional knowledge as well as the information flow design during the development of MDSS, along with the relationship among application areas.

  • 76.
    Gandhi, Kanika
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Towards data mining based decision support in manufacturing maintenance2018In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 261-265Article in journal (Refereed)
    Abstract [en]

    The current work presents a decision support system architecture for evaluating the features representing the health status to predict maintenance actions and remaning useful life of component. The evaluation is possible through pattern analysis of past and current measurements of the focused research components. Data mining visualization tools help in creating the most suitable patterns and learning insights from them. Estimations like features split values or measurement frequency of the component is achieved through classification methods in data mining. This paper presents how the quantitative results generated from data mining can be used to support decision making of domain experts.

  • 77.
    Givehchi, Mohammad
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Ng, Amos H. C.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Spot-welding sequence planning and optimization using a hybrid rule-based approach and genetic algorithm2011In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 27, no 4, p. 714-722Article in journal (Refereed)
    Abstract [en]

    Performing assembly planning to find a valid hierarchical assembling structure of a product (i.e. Manufacturing Bill of Materials or MBOM) based on the constraints and necessities inferred from or declared by different sources is potentially complicated. On the other hand, Engineering Changes (EC) may drastically affect the constraints and necessities which the planning of an MBOM was based on. Managing ECs to evaluate and propagate their effects on the upstream data used in assembly planning and downstream activities and information is crucial but problematic. Often it is possible to define a set of rules for the constraints and necessities of assembly planning and find solutions or check validity of solutions based on the rule-set. This paper proposes a rule-based assembly planning method and introduces the concepts and standard notations on how structured rule-sets can be derived from descriptive rules and then used in an algorithm for generating or validating MBOMs. The method was partially automated and successfully employed along with a commercial Virtual Manufacturing package integrated with an in-house developed GA-based sequence optimizer and applied to the sequence optimization in minimizing the cycle time of the robotic spot welding operations for a sheet-metal assembly found in automotive industry. (C) 2011 Elsevier Ltd. All rights reserved.

  • 78.
    Givehchi, Mohammad
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    An Evolutionary Operation Sequence Optimization Tool for Robotic Spot Welding Based on Collision-Free Path Planner in Virtual Manufacturing2011In: Proceedings of NAMRI/SME, Society of Manufacturing Engineers, North American Manufacturing Research Institution, NAMRI/SME , 2011, p. 88-98Conference paper (Refereed)
    Abstract [en]

    Many problems in the lifecycle of Product and Production Development (PPD) can be formulated as optimization problems. But in most of the real-world cases, they are too complex to be solved by analytical models or classical optimization methods. CAx and Virtual Manufacturing (VM) tools are on the other hand being employed to create virtual representation of products and processes before any physical realization is conducted. Synergy of these two domains is of interest in this paper where planning a process with the minimum cycle-time for assembling a spot welded sheet-metal product is desired. The methodology suggests an extendible virtual manufacturing-based optimization approach using evolutionary algorithms. Accordingly, a novel toolset with integration of evolutionary optimization and a commercial VM environment is developed. More specifically, the latest feature which takes advantage of the collision avoidant segment path planning functionality of the VM tool and integrates it with the sequence optimizer is described.

  • 79.
    Givehchi, Mohammad
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    An Integrated Approach to Spot Welding Sequence Planning and Optimization2010In: Proceedings of the ASME 2010 International Manufacturing Science and Engineering Conference: Volume 2, New York: ASME Press, 2010, p. 543-551Conference paper (Refereed)
    Abstract [en]

    Almost   in   every   discipline   involved   in   Product   and Production Development (PPD), optimization problems arrive. These  real-world  problems  are  too  complex  to  be  solved  by analytical models and classical optimization methods. CAx and Virtual Manufacturing (VM) tools are on the other hand being employed   more  and   more  to   create   virtual  representation models  of  the  products  under  development  and  their  related production   facilities,   processes,   and   systems   in   a   virtual environment  before  any  physical  realization  is  conducted. Synergy of these two domains is of interest in this paper where a PPD problem requiring planning a process with the minimum cycle-time  for  assembling  a  spot  welded  sheet-metal  product was  solved.  The  methodology  suggests  an  extendible  virtual manufacturing-based optimization approach using evolutionary algorithms.  The  methodology  is  also  required  to  be  partially compliant   to   the   concept   of   integrated   Product-Process-Resource  planning  and  optimization.  An  optimization  tool  is developed  accordingly  for  operation  sequence  optimization integrated with a commercial VM environment.

  • 80.
    Givehchi, Mohammad
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Evolutionary optimization of robotic assembly operation sequencing with collision-free paths2011In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 30, no 4, p. 196-203Article in journal (Refereed)
    Abstract [en]

    Many problems in the lifecycle of product and production development (PPD) can be formulated as optimization problems. But in most of the real-world cases, they are too complex to be solved by analytical models or classical optimization methods. CAx and virtual manufacturing (VM) tools are on the other hand being employed to create virtual representation of products and processes before any physical realization is conducted. Synergy of these two domains is of interest in this paper where planning a process with the minimum cycle-time for assembling a spot welded sheet-metal product is desired. The methodology suggests an extendible virtual manufacturing-based optimization approach using evolutionary algorithms. Accordingly, a novel toolset with integration of evolutionary optimization and a commercial VM environment is developed. More specifically, the latest feature which takes advantage of the collision avoidant segment path planning functionality of the VM tool and integrates it with the sequence optimizer is described. (C) 2011 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

  • 81.
    Givehchi Yazdi, Mohammad
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Operation Sequence Optimization using an extended Virtual Manufacturing tool2011In: Proceedings of the 4th Swedish Production Symposium, Lund, 2011, p. 383-390Conference paper (Refereed)
  • 82.
    Goienetxea, Ainhoa
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H.C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Division of Industrial Engineering and Management, Department of Engineering Science, Uppsala University, Sweden.
    Urenda Moris, Matías
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Division of Industrial Engineering and Management, Department of Engineering Science, Uppsala University, Sweden.
    Bringing together Lean and simulation: a comprehensive review2019In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588XArticle, review/survey (Refereed)
    Abstract [en]

    Lean is and will still be one of the most popular management philosophies in the Industry 4.0 context and simulation is one of its key technologies. Many authors discuss about the benefits of combining Lean and simulation to better support decision makers in system design and improvement. However, there is a lack of reviews in the domain. Therefore, this paper presents a four-stage comprehensive review and analysis of existing literature on their combination. The aim is to identify the state of the art, existing methods and frameworks for combining Lean and simulation, while also identifying key research perspectives and challenges. The main trends identified are the increased interest in the combination of Lean and simulation in the Industry 4.0 context and in their combination with optimisation, Six Sigma, as well as sustainability. The number of articles in these areas is likely to continue to grow. On the other hand, we highlight six gaps found in the literature regarding the combination of Lean and simulation, which may induce new research opportunities. Existing technical, organisational, as well as people and culture related challenges on the combination of Lean and simulation are also discussed.

  • 83.
    Goienetxea Uriarte, Ainhoa
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Industrial Engineering and Management, School of Engineering, Jönköping University, Sweden.
    Ruiz Zúñiga, Enrique
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Urenda Moris, Matías
    Department of Industrial Engineering and Management, School of Engineering, Jönköping University, Sweden.
    Improving the Material Flow of a Manufacturing Company via Lean, Simulation and Optimization2017In: Proceedings of the International Conference on Industrial Engineering and Engineering Management, IEEM2017, IEEE, 2017, p. 1245-1250Conference 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.

  • 84.
    Goienetxea Uriarte, Ainhoa
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Industrial Engineering and Management, School of Engineering, Jönköping University, Sweden.
    Urenda Moris, Matias
    Department of Industrial Engineering and Management, School of Engineering, Jönköping University, Sweden.
    Jägstam, Mats
    Department of Industrial Engineering and Management, School of Engineering, Jönköping University, Sweden.
    Lean, Simulation and Optimization: A maturity model2017In: Proceedings of the International Conference on Industrial Engineering and Engineering Management, IEEM2017, IEEE, 2017, p. 1310-1315Conference 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.

  • 85.
    Goienetxea Uriarte, Ainhoa
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Urenda Moris, Matías
    Division of Industrial Engineering and Management, Department of Engineering Science, Uppsala University, Uppsala, Sweden.
    Supporting the lean journey with simulation and optimization in the context of Industry 4.02018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 25, p. 586-593Article in journal (Refereed)
    Abstract [en]

    The new industrial revolution brings important changes to organizations that will need to adapt their machines, systems and employees’ competences to sustain their business in a highly competitive market. Management philosophies such as lean will also need to adapt to the improvement possibilities that Industry 4.0 brings. This paper presents a review on the role of lean and simulation in the context of Industry 4.0. Additionally, the paper presents a conceptual framework where simulation and optimization will make the lean approach more efficient, speeding up system improvements and reconfiguration, by means of an enhanced decision-making process and supported organizational learning.

  • 86.
    Goienetxea Uriarte, Ainhoa
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ruiz Zúñiga, Enrique
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Urenda Moris, Matías
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    How can decision makers be supported in the improvement of an emergency department?: A simulation, optimization and data mining approach2017In: Operations Research for Health Care, ISSN 2211-6923, E-ISSN 2211-6931, Vol. 15, p. 102-122Article in journal (Refereed)
    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.

  • 87.
    Goienetxea Uriarte, Ainhoa
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ruiz Zúñiga, Enrique
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Urenda Moris, Matías
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    System design and improvement of an emergency department using Simulation-Based Multi-Objective Optimization2015In: Journal of Physics, Conference Series, ISSN 1742-6588, E-ISSN 1742-6596, Vol. 616, no 1, article id 012015Article in journal (Refereed)
    Abstract [en]

    Discrete Event Simulation (DES) is nowadays widely used to support decision makers in system analysis and improvement. However, the use of simulation for improving stochastic logistic processes is not common among healthcare providers. The process of improving healthcare systems involves the necessity to deal with trade-off optimal solutions that take into consideration a multiple number of variables and objectives. Complementing DES with Multi-Objective Optimization (SMO) creates a superior base for finding these solutions and in consequence, facilitates the decision-making process. This paper presents how SMO has been applied for system improvement analysis in a Swedish Emergency Department (ED). A significant number of input variables, constraints and objectives were considered when defining the optimization problem. As a result of the project, the decision makers were provided with a range of optimal solutions which reduces considerably the length of stay and waiting times for the ED patients. SMO has proved to be an appropriate technique to support healthcare system design and improvement processes. A key factor for the success of this project has been the involvement and engagement of the stakeholders during the whole process.

  • 88.
    Goienetxea Uriarte, Ainhoa
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ruiz Zúñiga, Enrique
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Urenda Moris, Matías
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Karlberg, Catarina
    Monitoring and Analysis Area, Health Department of Västra Götaland, Skövde, Sweden.
    Wallqvist, Pierre
    Monitoring and Analysis Area, Health Department of Västra Götaland, Skövde, Sweden.
    Improved system design of an emergency department through simulation-based multiobjective-optimization2014Conference paper (Refereed)
    Abstract [en]

    Healthcare facilities, and especially emergency departments (ED), are usually characterized by its complexity due to the variability and stochastic nature of the processes involved in the system. The combination of different flows of patients, staff and resources also increments the complexity of this kind of facilities. In order to increase its efficiency, many researchers have proposed discrete-event simulation (DES) as a powerful improvement tool. However, DES can be a limited approach in the case a simulation model has too many combinations of input parameters, complex correlations between the input and output parameters and different objective functions. Hence, to find the best configuration of a complex system, an approach combining DES and meta-heuristic optimization becomes an even more powerful improvement technique. Simulation-based multiobjective-optimization (SMO) is a promising approach to generate multiple trade-off solutions particularly when multiple conflicting objectives exist within a complex system. The generated solutions provide decision makers with feasible and optimal alternatives to improve, modify or design healthcare systems. The aim of this paper is to present the work done at the ED of the regional Hospital of Skövde in Sweden, where SMO implemented in modeFromtier has been successfully applied. The result and methodology present a successful approach for decision makers in healthcare systems to reduce the waiting time of patients saving considerable time, money and resources.

  • 89.
    Goienetxea Uriarte, Ainhoa
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Sellgren, Tommy
    Volvo Car Corporation.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Urenda Moris, Matías
    Uppsala University.
    Introducing simulation and optimization in the Lean continuous improvement standards in an automotive company2019In: Proceedings of the Winter Simulation Conference, Gothenburg, December 9-12, 2018 / [ed] M. Rabe, A. A. Juan, N. Mustafee, A. Skoogh, S. Jain, B. Johansson, Piscataway, New Jersey: IEEE, 2019, p. 3352-3363Conference paper (Refereed)
    Abstract [en]

    The highly competitive automobile market requires automotive companies to become efficient by continuously improving their production systems. This paper presents a case study where simulationbased optimization (SBO) was employed as a step within a Value Stream Mapping event. The aim of the study was to promote the use of SBO to strengthen the continuous improvement work of the company. The paper presents all the key steps performed in the study, including the challenges faced and a reflection on how to introduce SBO as a powerful tool within the lean continuous improvement standards.

  • 90.
    Goienetxea Uriarte, Ainhoa
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Urenda Moris, Matías
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Oscarsson, Jan
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Lean, Simulation and Optimization: A Win-Win combination2016In: Proceedings of the 2015 Winter Simulation Conference / [ed] L. Yilmaz, W. K. V. Chan, I. Moon, T. M. K. Roeder, C. Macal, and M. D. Rossetti, Piscataway, New Jersey: IEEE Computer Society, 2016, p. 2227-2238Conference paper (Refereed)
    Abstract [en]

    Lean and simulation analysis are driven by the same objective, how to better design and improve processes making the companies more competitive. The adoption of lean has been widely spread in companies from public to private sectors and simulation is nowadays becoming more and more popular. Several authors have pointed out the benefits of combining simulation and lean, however, they are still rarely used together in practice. Optimization as an additional technique to this combination is even a more powerful approach especially when designing and improving complex processes with multiple conflicting objectives. This paper presents the mutual benefits that are gained when combining lean, simulation and optimization and how they overcome each other´s limitations. A framework including the three concepts, some of the barriers for its implementation and a real-world industrial example are also described.

  • 91.
    Hedenstierna, Philip
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    On the placement of the customer order decoupling point2010In: Proceedings of 2010 8th International Conference on Supply Chain Management and Information Systems: Logistics Systems and Engineering, Hong Kong: The Hong Kong Polytechnic University , 2010, p. Article number 5681729-Conference paper (Refereed)
    Abstract [en]

    It is often suggested that supply chains should start working directly towards customer orders as far upstream as possible, mostly for inventory reduction reasons. However, the customer order decoupling point (CODP) cannot be pushed further upstream than customers are willing to wait. In this paper, we use a system dynamics model to show that the optimal placement of the CODP depends on the demand signal. Our findings indicate that placing the CODP downstream allows for short-term fluctuations in demand to be absorbed by the order book, leading to a stable production rate. This benefit must however be weighed against any additional safety stock a CODP placed far downstream may require.

  • 92.
    Hedenstierna, Philip
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H.C.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Dynamic implications of customer order decoupling point positioning2011In: Journal of Manufacturing Technology Management, ISSN 1741-038X, E-ISSN 1758-7786, Vol. 22, no 8, p. 1032-1042Article in journal (Refereed)
    Abstract [en]

    Purpose: The positioning of the customer order decoupling point (CODP) is an important strategic consideration for supply chains. Recently, research has focused only on the static effects of CODP positioning. The purpose of this paper is to expand the body of knowledge by describing the dynamic consequences that arise from shifting the CODP upstream or downstream.

    Design/methodology/approach: A generic assembly-to-order system dynamics simulation model is developed and used to evaluate the dynamic consequences of shifting the CODP.

    Findings: Placing the CODP downstream allows for short-term fluctuations in demand to be absorbed by the order book, leading to a stable production rate and inventory response. This benefit must, however, be weighed against any additional safety stock a CODP placed far downstream may require.

    Research limitations/implications: The paper demonstrates the importance of considering the dynamic aspects of CODP positioning. Further research should investigate the phenomenon for different demand scenarios and supply chain configurations.

    Practical implications: Downstream shifting of the CODP has been identified as a powerful way to reduce variability in assembly-to-order systems.

    Originality/value: This paper introduces the dynamic consequences of CODP location, providing a new perspective that should be considered when positioning the CODP.

  • 93.
    Holm, Magnus
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Doverborn, Josefine
    Ng, Amos
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    De Vin, Leo
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Optimisation of Operation Sequences in Flexible Manufacturing Cells using Virtual Manufacturing Tools2009In: Proceedings of the 19th International Conference on Flexible Automation and Intelligent Manufacturing / [ed] Farhad Nabhani, Teesside University , 2009, p. 1348-1355Conference paper (Refereed)
    Abstract [en]

    Manufacturing organisations are continuously forced to improve the way of working to maintain their competitiveness on the global market. To optimize a production facility requires not only an optimal design of the whole line but also its internal operations sequencing and scheduling during the operational phase. The use of Virtual Manufacturing tools such as Discrete Event Simulation and Computer Aided Robotics has been proven to be highly effective both for production system design and for operational analysis and improvement. This paper proposes a new optimisation method, named SIMBOSeer, which synergistically combines the areas of optimisation, flexibility and virtual manufacturing that integrates robot simulation with simulation-based optimisation. Evaluation of SIMBOSeer, as applied to an existing manufacturing cell at a powertrain manufacturing company in Sweden, has shown that it can be used as an iterative process of analysis and optimisation. The results, when using realistic what-if scenarios, clearly point out that SIMBOSeer can facilitate the optimisation of operation sequences and decrease the total cycle time of the manufacturing cell. This is due to the fact that many non-value adding functions, such as unnecessary tool changes, which have a great negative impact on the effectiveness of the flexible manufacturing cell, can be avoided. Whilst the use of SIMBOSeer has obvious advantages under normal operating conditions of the cell, its use become even more beneficial when disturbance like tool failures occur or when product variants are introduced to the cell.

  • 94.
    Hossain, Mosharraf
    et al.
    The Royal Institute of Technology (KTH), Department of Production Engineering, School of Industrial Engineering and Management, Stockholm, Sweden.
    Harari, Natalia
    The Royal Institute of Technology (KTH), Department of Production Engineering, School of Industrial Engineering and Management, Stockholm, Sweden.
    Semere, Daniel
    The Royal Institute of Technology (KTH), Department of Production Engineering, School of Industrial Engineering and Management, Stockholm, Sweden.
    Mårtensson, Pär
    Scania SPS & Industrial Development.
    Ng, Amos. H. C.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Andersson, Martin
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Integrated Modeling and Application of Standardized Data Schema2012In: Proceedings of the 5th Swedish Production Symposium (SPS 12), 2012, p. 473-478Conference paper (Refereed)
  • 95.
    Karlsson, Ingemar
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Bernedixen, Jacob
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Pehrsson, Leif
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Combining augmented reality and simulation-based optimization for decision support in manufacturing2017In: 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), 2017, p. 3988-3999Conference 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. 

  • 96.
    Karlsson, Ingemar
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Ng, Amos H. C.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Aslam, Tehseen
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Dudas, Catarina
    Volvo Group Trucks Operations.
    An Interactive, Cloud-Based Simulation Optimization System for Knowledge Discovery and Decision Support In Manufacturing2014In: Proceedings of the sixth Swedish Production Symposium, 2014, 2014Conference 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.

  • 97.
    Karlsson, Ingemar
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Bandaru, Sunith
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    An interactive decision support system using simulation-based optimization and data mining2015In: Proceedings of the 2015 Winter Simulation Conference / [ed] L. Yilmaz, W. K. V. Chan, I. Moon, T. M. K. Roeder, C. Macal, and M. D. Rossetti, IEEE Press, 2015, p. 2112-2123Conference paper (Refereed)
    Abstract [en]

    This paper describes a decision support system (DSS) built on knowledge extraction using simulation-based optimization and data mining. The paper starts with a requirements analysis based on a survey conducted with a number of industrial companies about their practices of using simulations for decision support.Based upon the analysis, a new, interactive DSS that can fulfill the industrial requirements, is proposed.The design of the cloud-based system architecture of the DSS is then described. To show the functionality and potential of the proposed DSS, an application study has been performed for the optimal design of a hypothetical but realistic flexible production cell. How important knowledge with respect to different preferences of the decision maker can be generated as rules, using the new Flexible Pattern Mining algorithm provided in the DSS, will be revealed by the results of this application study.

  • 98.
    Karlsson, Ingemar
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Interactive and Intelligent Decision Support in Manufacturing using Simulation Based Innovization and Cloud Computing2014In: Industrial Simulation Conference, Skövde, June 11-13, 2014, 2014, p. 69-74Conference paper (Refereed)
    Abstract [en]

    Simulation-based innovization is a method for extracting knowledge from a simulation model and optimization. This method can help decision makers to make high-quality decisions for their manufacturing systems so as to enhance the competitiveness of companies. Nevertheless, the simulation-based innovization process can be computationally costly and having these resources in-house can be expensive. By running the process in a cloud environment instead, the company only pays for the resources they are using. This paper proposes the concept of a cloud-based computing platform that can run the simulation-based innovization process and discuss its possibilities and challenges.

  • 99.
    Lidberg, Simon
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Volvo Car Corporation, Sweden.
    Aslam, Tehseen
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Pehrsson, Leif
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Evaluating the impact of changes on a global supply chain using an iterative approach in a proof-of-concept model2018In: Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden / [ed] Peter Thorvald, Keith Case, Amsterdam: IOS Press, 2018, p. 467-472Conference paper (Refereed)
    Abstract [en]

    Analyzing networks of supply-chains, where each chain is comprised of several actors with different purposes and performance measures, is a difficult task. There exists a large potential in optimizing supply-chains for many companies and therefore the supply-chain optimization problem is of great interest to study. To be able to optimize the supply-chain on a global scale, fast models are needed to reduce computational time. Previous research has been made into the aggregation of factories, but the technique has not been tested against supply-chain problems. When evaluating the configuration of factories and their inter-transportation on a global scale, new insights can be gained about which parameters are important and how the aggregation fits to a supply-chain problem. The paper presents an interactive proof-of-concept model enabling testing of supply chain concepts by users and decision makers.

  • 100.
    Lidberg, Simon
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Aslam, Tehseen
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Pehrsson, Leif
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Optimizing real-world factory flows using aggregated discrete event simulation modelling: Creating decision-support through simulation-based optimization and knowledge-extraction2019In: Flexible Services and Manufacturing Journal, ISSN 1936-6582, E-ISSN 1936-6590Article in journal (Refereed)
    Abstract [en]

    Reacting quickly to changing market demands and new variants by improving and adapting industrial systems is an important business advantage. Changes to systems are costly; especially when those systems are already in place. Resources invested should be targeted so that the results of the improvements are maximized. One method allowing this is the combination of discrete event simulation, aggregated models, multi-objective optimization, and data-mining shown in this article. A real-world optimization case study of an industrial problem is conducted resulting in lowering the storage levels, reducing lead time, and lowering batch sizes, showing the potential of optimizing on the factory level. Furthermore, a base for decision-support is presented, generating clusters from the optimization results. These clusters are then used as targets for a decision tree algorithm, creating rules for reaching different solutions for a decision-maker to choose from. Thereby allowing decisions to be driven by data, and not by intuition. 

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