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Publications (10 of 188) Show all publications
Nourmohammadi, A., Fathi, M., Ruiz Zúñiga, E. & Ng, A. H. C. (2019). A Genetic Algorithm for Bi-Objective Assembly Line Balancing Problem. In: Yan Jin, Mark Price (Ed.), Advances in Manufacturing Technology XXXIII: Proceedings of the 17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, September 10–12, 2019, Queen’s University Belfast, UK. Paper presented at 17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, September 10–12, 2019, Queen’s University Belfast, UK (pp. 519-524). Amsterdam: IOS Press, 9
Open this publication in new window or tab >>A Genetic Algorithm for Bi-Objective Assembly Line Balancing Problem
2019 (English)In: Advances in Manufacturing Technology XXXIII: Proceedings of the 17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, September 10–12, 2019, Queen’s University Belfast, UK / [ed] Yan Jin, Mark Price, Amsterdam: IOS Press, 2019, Vol. 9, p. 519-524Conference paper, Published paper (Refereed)
Abstract [en]

Assembly line designs in manufacturing commonly face the key problem of dividing the assembly tasks among the working stations so that the efficiency of the line is optimized. This problem is known as the assembly line balancing problem which is known to be NP-hard. This study, proposes a bi-objective genetic algorithm to cope with the assembly line balancing problem where the considered objectives are the utilization of the assembly line and the workload smoothness measured as the line efficiency and the variation of workload, respectively. The performance of the proposed genetic algorithm is tested through solving a set of standard problems existing in the literature. The computational results show that the genetic algorithm is promising in providing good solutions to the assembly line balancing problem.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2019
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 9
Keywords
Assembly line balancing, bi-objectives, Genetic Algorithm
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-17679 (URN)10.3233/ATDE190091 (DOI)978-1-64368-008-8 (ISBN)978-1-64368-009-5 (ISBN)
Conference
17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, September 10–12, 2019, Queen’s University Belfast, UK
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: 2019-09-13 Created: 2019-09-13 Last updated: 2019-09-16Bibliographically approved
Linnéusson, G., Ng, A. H. C. & Aslam, T. (2019). 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
2019 (English)In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860Article in journal (Refereed) Epub ahead of print
Abstract [en]

Managing maintenance and its impact on business results is increasingly complex, calling for more advanced operational research methodologies to address the challenge of sustainable decision-making. This problem-based research has identified a framework of methods to supplement the operations research/management science literature by contributing a hybrid simulation-based optimization framework (HSBOF), extending previously reported research.

Overall, it is the application of multi-objective optimization (MOO) with system dynamics (SD) and discrete-event simulation (DES) respectively which allows maintenance activities to be pinpointed in the production system based on analyzes generating less reactive work load on the maintenance organization. Therefore, the application of the HSBOF informs practice by a multiphase process, where each phase builds knowledge, starting with exploring feedback behaviors to why certain near-optimal maintenance behaviors arise, forming the basis of potential performance improvements, subsequently optimized using DES+MOO in a standard software, prioritizing the sequence of improvements in the production system for maintenance to implement.

Studying literature on related hybridizations using optimization the proposed work can be considered novel, being based on SD+MOO industrial cases and their application to a DES+MOO software.

Keywords
Problem structuring, Decision support, System dynamics, Multi-objective optimization, Discrete-event simulation
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)10.1016/j.ejor.2019.08.036 (DOI)
Available from: 2018-04-16 Created: 2018-04-16 Last updated: 2019-09-24Bibliographically approved
Fathi, M., Nourmohammadi, A., Ng, A. H. C., Syberfeldt, A. & Eskandari, H. (2019). An improved genetic algorithm with variable neighborhood search to solve the assembly line balancing problem. Engineering computations
Open this publication in new window or tab >>An improved genetic algorithm with variable neighborhood search to solve the assembly line balancing problem
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2019 (English)In: Engineering computations, ISSN 0264-4401, E-ISSN 1758-7077Article in journal (Refereed) Epub ahead of print
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.
Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2019
Keywords
assembly line balancing, genetic algorithm, variable neighborhood search, generation transfer
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-17157 (URN)10.1108/EC-02-2019-0053 (DOI)2-s2.0-85071617279 (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: 2019-06-18 Created: 2019-06-18 Last updated: 2019-09-24Bibliographically approved
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: 2019-05-09Bibliographically approved
Goienetxea, A., Ng, A. H. .. & Urenda Moris, M. (2019). Bringing together Lean and simulation: a comprehensive review. International Journal of Production Research
Open this publication in new window or tab >>Bringing together Lean and simulation: a comprehensive review
2019 (English)In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588XArticle, review/survey (Refereed) Epub ahead of print
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.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2019
Keywords
Lean, simulation, review, framework, discrete event simulation
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-17493 (URN)10.1080/00207543.2019.1643512 (DOI)000477234000001 ()
Funder
Knowledge Foundation
Available from: 2019-08-05 Created: 2019-08-05 Last updated: 2019-08-20Bibliographically approved
Nourmohammadi, A., Fathi, M. & Ng, A. H. C. (2019). Choosing efficient meta-heuristics to solve the assembly line balancing problem: A landscape analysis approach. Paper presented at 52nd CIRP Conference on Manufacturing Systems, Ljubljana, Slovenia, June 12-14, 2019. Procedia CIRP, 81, 1248-1253
Open this publication in new window or tab >>Choosing efficient meta-heuristics to solve the assembly line balancing problem: A landscape analysis approach
2019 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 81, p. 1248-1253Article in journal (Refereed) Published
Abstract [en]

It is widely known that the assembly line balancing problem (ALBP) is an NP-hard optimization problem. Although different meta-heuristics have been proposed for solving this problem so far, there is no convincing support that what type of algorithms can perform more efficiently than the others. Thus, using some statistical measures, the landscape of the simple ALBP is studied for the first time in the literature. The results indicate a flat landscape for the problem where the local optima are uniformly scattered over the search space. Accordingly, the efficiency of population-based algorithms in addressing the considered problem is statistically validated.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Assembly line balancing, fitness landscape analysis, meta-heuristic algorithms
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-17280 (URN)10.1016/j.procir.2019.03.302 (DOI)2-s2.0-85068480053 (Scopus ID)
Conference
52nd CIRP Conference on Manufacturing Systems, Ljubljana, Slovenia, June 12-14, 2019
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: 2019-06-24 Created: 2019-06-24 Last updated: 2019-08-19Bibliographically 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: 2019-07-02Bibliographically approved
Morshedzadeh, I., Ng, A. H. C. & Amouzgar, K. (2019). Management of virtual models with provenance information in the context of product lifecycle management: industrial case studies (1ed.). In: John Stark (Ed.), Product Lifecycle Management (Volume 4): The Case Studies (pp. 153-170). Cham: Springer
Open this publication in new window or tab >>Management of virtual models with provenance information in the context of product lifecycle management: industrial case studies
2019 (English)In: Product Lifecycle Management (Volume 4): The Case Studies / [ed] John Stark, Cham: Springer, 2019, 1, p. 153-170Chapter in book (Refereed)
Abstract [en]

Using virtual models instead of physical models can help industries reduce the time and cost of developments, despite the time consuming process of building virtual models. Therefore, reusing previously built virtual models instead of starting from scratch can eliminate a large amount of work from users. Is having a virtual model enough to reuse it in another study or task? In most cases, not. Information about the history of that model makes it clear for the users to decide if they can reuse this model or to what extent the model is needed to be modified. A provenance management system (PMS) has been designed to manage provenance information, and it has been used with product lifecycle management system (PLM) and computer-aided technologies (CAx) to save and present historical information about a virtual model. This chapter presents a sequence-based framework of the CAx-PLM-PMS chain and two application case studies considering the implementation of this framework.

Place, publisher, year, edition, pages
Cham: Springer, 2019 Edition: 1
Series
Decision Engineering, ISSN 1619-5736, E-ISSN 2197-6589
Keywords
Virtual models, Provenance, Product lifecycle management, virtual models, CAx, Discrete event simulation, Meta model, Cutting simulation
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-17765 (URN)10.1007/978-3-030-16134-7_13 (DOI)978-3-030-16133-0 (ISBN)978-3-030-16134-7 (ISBN)
Projects
knowledge-driven decision making in Swedish industry (KDDS)
Available from: 2019-10-07 Created: 2019-10-07 Last updated: 2019-10-07Bibliographically approved
Amouzgar, K., Bandaru, S., Andersson, T. & Ng, A. H. C. (2019). Metamodel based multi-objective optimization of a turning process by using finite element simulation. Engineering optimization (Print)
Open this publication in new window or tab >>Metamodel based multi-objective optimization of a turning process by using finite element simulation
2019 (English)In: Engineering optimization (Print), ISSN 0305-215X, E-ISSN 1029-0273Article in journal (Refereed) Epub ahead of print
Abstract [en]

This study investigates the advantages and potentials of the metamodelbased multi-objective optimization (MOO) of a turning operation through the application of finite element simulations and evolutionary algorithms to a metal cutting process. The objectives are minimizing the interface temperature and tool wear depth obtained from FE simulations using DEFORM2D software, and maximizing the material removal rate. Tool geometry and process parameters are considered as the input variables. Seven metamodelling methods are employed and evaluated, based on accuracy and suitability. Radial basis functions with a priori bias and Kriging are chosen to model tool–chip interface temperature and tool wear depth, respectively. The non-dominated solutions are found using the strength Pareto evolutionary algorithm SPEA2 and compared with the non-dominated front obtained from pure simulation-based MOO. The metamodel-based MOO method is not only advantageous in terms of reducing the computational time by 70%, but is also able to discover 31 new non-dominated solutions over simulation-based MOO.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2019
Keywords
Metamodeling, Surrogate models, Machining, Turning, Multi-objective optimization
National Category
Mechanical Engineering
Research subject
Production and Automation Engineering; Virtual Manufacturing Processes
Identifiers
urn:nbn:se:his:diva-17520 (URN)10.1080/0305215X.2019.1639050 (DOI)000477101800001 ()
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-10-02Bibliographically approved
Lidberg, S., Aslam, T., Pehrsson, L. & Ng, A. H. C. (2019). Optimizing real-world factory flows using aggregated discrete event simulation modelling: Creating decision-support through simulation-based optimization and knowledge-extraction. Flexible Services and Manufacturing Journal
Open this publication in new window or tab >>Optimizing real-world factory flows using aggregated discrete event simulation modelling: Creating decision-support through simulation-based optimization and knowledge-extraction
2019 (English)In: Flexible Services and Manufacturing Journal, ISSN 1936-6582, E-ISSN 1936-6590Article in journal (Refereed) Epub ahead of print
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. 

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Aggregation, Data mining, Decision support, Discrete event simulation, Industrial case study, Multi-objective optimization, Agglomeration, Decision making, Decision support systems, Decision trees, Digital storage, Multiobjective optimization, Trees (mathematics), Decision supports, Decision-tree algorithm, Industrial problem, Industrial systems, Knowledge extraction, Real-world optimization, Simulation-based optimizations
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-17480 (URN)10.1007/s10696-019-09362-7 (DOI)2-s2.0-85068764729 (Scopus ID)
Available from: 2019-07-25 Created: 2019-07-25 Last updated: 2019-08-19Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0111-1776

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