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Fathi, M., Nourmohammadi, A., Ng, A. H. C. & Syberfeldt, A. (2019). An optimization model for balancing assembly lines with stochastic task times and zoning constraints. IEEE Access, 7, 32537-32550, Article ID 8663269.
Open this publication in new window or tab >>An optimization model for balancing assembly lines with stochastic task times and zoning constraints
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 32537-32550, article id 8663269Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
assembly line balancing, mathematical programming, stochastic, zoning constraints
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16689 (URN)10.1109/ACCESS.2019.2903738 (DOI)000463040400001 ()2-s2.0-85063577558 (Scopus ID)
Projects
This study is supported by the European Union’s Horizon 2020 research and innovation program under grant agreement no. 723711 through the MANUWORK project.
Funder
EU, Horizon 2020, 723711
Available from: 2019-03-09 Created: 2019-03-09 Last updated: 2019-04-18Bibliographically approved
Goienetxea Uriarte, A., Sellgren, T., Ng, A. H. C. & Urenda Moris, M. (2019). Introducing simulation and optimization in the Lean continuous improvement standards in an automotive company. In: M. Rabe, A. A. Juan, N. Mustafee, A. Skoogh, S. Jain, B. Johansson (Ed.), Proceedings of the Winter Simulation Conference, Gothenburg, December 9-12, 2018: . Paper presented at Winter Simulation Conference, WSC 2018, Gothenburg, December 9-12, 2018 (pp. 3352-3363). Piscataway, New Jersey: IEEE
Open this publication in new window or tab >>Introducing simulation and optimization in the Lean continuous improvement standards in an automotive company
2019 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Piscataway, New Jersey: IEEE, 2019
Series
Winter Simulation Conference. Proceedings, ISSN 0891-7736, E-ISSN 1558-4305
Keywords
Lean, simulation, optimization, continuous improvement, automotive
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16566 (URN)10.1109/WSC.2018.8632403 (DOI)000461414103049 ()2-s2.0-85062610351 (Scopus ID)978-1-5386-6572-5 (ISBN)978-1-5386-6570-1 (ISBN)978-1-5386-6571-8 (ISBN)978-1-5386-6573-2 (ISBN)
Conference
Winter Simulation Conference, WSC 2018, Gothenburg, December 9-12, 2018
Available from: 2019-01-16 Created: 2019-01-16 Last updated: 2019-04-11Bibliographically approved
Bandaru, S. & Ng, A. H. C. (2019). Trend Mining: A Visualization Technique to Discover Variable Trends in the Objective Space. In: Kalyanmoy Deb, Erik Goodman, Carlos A. Coello Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, Patrick Reed (Ed.), Evolutionary Multi-Criterion Optimization: 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings. Paper presented at 10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019, East Lansing, MI, USA, March 10-13, 2019 (pp. 605-617). Cham, Switzerland: Springer, 11411
Open this publication in new window or tab >>Trend Mining: A Visualization Technique to Discover Variable Trends in the Objective Space
2019 (English)In: Evolutionary Multi-Criterion Optimization: 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings / [ed] Kalyanmoy Deb, Erik Goodman, Carlos A. Coello Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, Patrick Reed, Cham, Switzerland: Springer, 2019, Vol. 11411, p. 605-617Conference paper, Published paper (Refereed)
Abstract [en]

Practical multi-objective optimization problems often involve several decision variables that influence the objective space in different ways. All variables may not be equally important in determining the trade-offs of the problem. Decision makers, who are usually only concerned with the objective space, have a hard time identifying such important variables and understanding how the variables impact their decisions and vice versa. Several graphical methods exist in the MCDM literature that can aid decision makers in visualizing and navigating high-dimensional objective spaces. However, visualization methods that can specifically reveal the relationship between decision and objective space have not been developed so far. We address this issue through a novel visualization technique called trend mining that enables a decision maker to quickly comprehend the effect of variables on the structure of the objective space and easily discover interesting variable trends. The method uses moving averages with different windows to calculate an interestingness score for each variable along predefined reference directions. These scores are presented to the user in the form of an interactive heatmap. We demonstrate the working of the method and its usefulness through a benchmark and two engineering problems.

Place, publisher, year, edition, pages
Cham, Switzerland: Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11411
Keywords
Visualization, Data mining, Multi-criteria decision making, Decision space, Trend analysis, Objective space
National Category
Other Computer and Information Science
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16712 (URN)10.1007/978-3-030-12598-1_48 (DOI)2-s2.0-85063032277 (Scopus ID)978-3-030-12597-4 (ISBN)978-3-030-12598-1 (ISBN)
Conference
10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019, East Lansing, MI, USA, March 10-13, 2019
Projects
Knowledge-Driven Decision Support (KDDS)
Funder
Knowledge Foundation, 41231
Note

Also part of the Theoretical Computer Science and General Issues book sub series (LNTCS, volume 11411)

Available from: 2019-03-25 Created: 2019-03-25 Last updated: 2019-03-26
Amouzgar, K., Bandaru, S., Andersson, T. J. & Ng, A. H. C. (2018). A framework for simulation based multi-objective optimization and knowledge discovery of machining process. The International Journal of Advanced Manufacturing Technology, 98(9-12), 2469-2486
Open this publication in new window or tab >>A framework for simulation based multi-objective optimization and knowledge discovery of machining process
2018 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 98, no 9-12, p. 2469-2486Article in journal (Refereed) Published
National Category
Mechanical Engineering
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15136 (URN)10.1007/s00170-018-2360-8 (DOI)000444704300020 ()2-s2.0-85049664435 (Scopus ID)
Available from: 2018-05-09 Created: 2018-05-09 Last updated: 2019-03-14
Linnéusson, G., Ng, A. H. C. & Aslam, T. (2018). A hybrid simulation-based optimization framework for supporting strategic maintenance to improve production performance. European Journal of Operational Research
Open this publication in new window or tab >>A hybrid simulation-based optimization framework for supporting strategic maintenance to improve production performance
2018 (English)In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860Article in journal (Refereed) Submitted
National Category
Production Engineering, Human Work Science and Ergonomics Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15064 (URN)
Available from: 2018-04-16 Created: 2018-04-16 Last updated: 2019-03-19Bibliographically approved
Senington, R., Baumeister, F., Ng, A. & Oscarsson, J. (2018). A linked data approach for the connection of manufacturing processes with production simulation models. Paper presented at 28th CIRP Design Conference, Nantes, France, May 23-25, 2018. Procedia CIRP, 70, 440-445
Open this publication in new window or tab >>A linked data approach for the connection of manufacturing processes with production simulation models
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 70, p. 440-445Article in journal (Refereed) Published
Abstract [en]

This paper discusses the expected benefits of using linked data for the tasks of gathering, managing and understanding the data of smart factories. It has the further specific focus of using this data to maintaining a Digital Twin for the purposes of analysis and optimisation of such factories. The proposals are motivated by the use of an industrial example looking at the types of information required, the variation in data which is available and the requirements of an analysis platform to provide parameters for seamless, automated simulation and optimisation. 

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Smart Factory, Digital Twin, Linked Data
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16005 (URN)10.1016/j.procir.2018.03.243 (DOI)000437126800074 ()2-s2.0-85051264447 (Scopus ID)
Conference
28th CIRP Design Conference, Nantes, France, May 23-25, 2018
Projects
TWIN
Available from: 2018-07-20 Created: 2018-07-20 Last updated: 2018-11-09Bibliographically approved
Bandaru, S. & Ng, A. H. C. (2018). An empirical comparison of metamodeling strategies in noisy environments. In: Hernan Aguirre (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2018): . Paper presented at Genetic and Evolutionary Computation Conference (GECCO-2018), Kyoto, July 15th-19th 2018 (pp. 817-824). New York, NY, USA: ACM Digital Library, Article ID 3205509.
Open this publication in new window or tab >>An empirical comparison of metamodeling strategies in noisy environments
2018 (English)In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2018) / [ed] Hernan Aguirre, New York, NY, USA: ACM Digital Library, 2018, p. 817-824, article id 3205509Conference paper, Published paper (Refereed)
Abstract [en]

Metamodeling plays an important role in simulation-based optimization by providing computationally inexpensive approximations for the objective and constraint functions. Additionally metamodeling can also serve to filter noise, which is inherent in many simulation problems causing optimization algorithms to be mislead. In this paper, we conduct a thorough statistical comparison of four popular metamodeling methods with respect to their approximation accuracy at various levels of noise. We use six scalable benchmark problems from the optimization literature as our test suite. The problems have been chosen to represent different types of fitness landscapes, namely, bowl-shaped, valley-shaped, steep ridges and multi-modal, all of which can significantly influence the impact of noise. Each metamodeling technique is used in combination with four different noise handling techniques that are commonly employed by practitioners in the field of simulation-based optimization. The goal is to identify the metamodeling strategy, i.e. a combination of metamodeling and noise handling, that performs significantly better than others on the fitness landscapes under consideration. We also demonstrate how these results carry over to a simulation-based optimization problem concerning a scalable discrete event model of a simple but realistic production line.

Place, publisher, year, edition, pages
New York, NY, USA: ACM Digital Library, 2018
Series
GECCO '18
Keywords
simulation, optimization, metamodeling, noise
National Category
Computer Sciences Other Mechanical Engineering
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15966 (URN)10.1145/3205455.3205509 (DOI)2-s2.0-85050638821 (Scopus ID)978-1-4503-5618-3 (ISBN)
Conference
Genetic and Evolutionary Computation Conference (GECCO-2018), Kyoto, July 15th-19th 2018
Projects
Synergy KDDS
Funder
Knowledge Foundation, 41231
Available from: 2018-07-12 Created: 2018-07-12 Last updated: 2019-03-27
Fathi, M., Nourmohammadi, A. & Ng, A. H. C. (2018). Assembly Line Balancing Type-E with Technological Requirement: A Mathematical Model. In: Peter Thorvald, Keith Case (Ed.), Peter Thorvald, Keith Case (Ed.), 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. Paper presented at 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden (pp. 183-188). Amsterdam: IOS Press
Open this publication in new window or tab >>Assembly Line Balancing Type-E with Technological Requirement: A Mathematical Model
2018 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2018
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 8
Keywords
line balancing, type E, technological requirement, mathematical model
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16203 (URN)10.3233/978-1-61499-902-7-183 (DOI)000462212700030 ()2-s2.0-85057361294 (Scopus ID)978-1-61499-901-0 (ISBN)978-1-61499-902-7 (ISBN)
Conference
16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden
Available from: 2018-09-13 Created: 2018-09-13 Last updated: 2019-04-08Bibliographically approved
Ayani, M., Ganebäck, M. & Ng, A. H. C. (2018). Digital Twin: Applying emulation for machine reconditioning. Paper presented at 51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018. Procedia CIRP, 72, 243-248
Open this publication in new window or tab >>Digital Twin: Applying emulation for machine reconditioning
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 243-248Article in journal (Refereed) Published
Abstract [en]

Old machine reconditioning projects extend the life length of machines with reduced investments, however they frequently involve complex challenges. Due to the lack of technical documentation and the fact that the machines are running in production, they can require a reverse engineering phase and extremely short commissioning times. Recently, emulation software has become a key tool to create Digital Twins and carry out virtual commissioning of new manufacturing systems, reducing the commissioning time and increasing its final quality. This paper presents an industrial application study in which an emulation model is used to support a reconditioning project and where the benefits gained in the working process are highlighted.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
digital twin, emulation, virtual commissioning, industry 4.0, reconditioning, retrofitting
National Category
Control Engineering
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16078 (URN)10.1016/j.procir.2018.03.139 (DOI)2-s2.0-85049565560 (Scopus ID)
Conference
51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018
Projects
Twin
Available from: 2018-08-24 Created: 2018-08-24 Last updated: 2018-10-31Bibliographically approved
Lidberg, S., Aslam, T., Pehrsson, L. & Ng, A. H. C. (2018). Evaluating the impact of changes on a global supply chain using an iterative approach in a proof-of-concept model. In: Peter Thorvald, Keith Case (Ed.), 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. Paper presented at 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden (pp. 467-472). Amsterdam: IOS Press
Open this publication in new window or tab >>Evaluating the impact of changes on a global supply chain using an iterative approach in a proof-of-concept model
2018 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2018
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 8
Keywords
Aggregated modeling, Discrete Event Simulation, Manufacturing, Proof-of-concept, Supply-chain management, Decision making, Iterative methods, Manufacture, Supply chain management, Computational time, Global supply chain, Interactive proofs, Iterative approach, Performance measure, Proof of concept, Supply chain optimization, Industrial research
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; INF203 Virtual Machining
Identifiers
urn:nbn:se:his:diva-16496 (URN)10.3233/978-1-61499-902-7-467 (DOI)000462212700075 ()2-s2.0-85057354809 (Scopus ID)978-1-61499-901-0 (ISBN)978-1-61499-902-7 (ISBN)
Conference
16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden
Funder
Knowledge Foundation
Available from: 2018-12-13 Created: 2018-12-13 Last updated: 2019-04-08Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0111-1776

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