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Nourmohammadi, A., Fathi, M. & Ng, A. H. C. (2024). Balancing and scheduling human-robot collaborated assembly lines with layout and objective consideration. Computers & industrial engineering, 187, Article ID 109775.
Open this publication in new window or tab >>Balancing and scheduling human-robot collaborated assembly lines with layout and objective consideration
2024 (English)In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 187, article id 109775Article in journal (Refereed) Published
Abstract [en]

The recent Industry 4.0 trend, followed by the technological advancement of collaborative robots, has urged many industries to shift towards new types of assembly lines with human-robot collaboration (HRC). This type of manufacturing line, in which human skill is supported by robot agility, demands an integrated balancing and scheduling of tasks and operators among the stations. This study attempts to deal with these joint problems in the straight and U-shaped assembly lines while considering different objectives, namely, the number of stations (Type-1), the cycle time (Type-2), and the cost of stations, operators, and robot energy consumption (Type-rw). The latter type often arises in the real world, where multiple types of humans and robots with different skills and energy levels can perform the assembly tasks collaboratively or in parallel at stations. Additionally, practical constraints, namely robot tool changes, zoning, and technological requirements, are considered in Type-rw. Accordingly, different mixed-integer linear programming (MILP) models for straight and U-shaped layouts are proposed with efficient lower and upper bounds for each objective. The computational results validate the efficiency of the proposed MILP model with bounded objectives while addressing an application case and different test problem sizes. In addition, the analysis of results shows that the U-shaped layout offers greater flexibility than the straight line, leading to more efficient solutions for JIT production, particularly in objective Type-2 followed by Type-rw and Type-1. Moreover, the U-shaped lines featuring a high HRC level can further enhance the achievement of desired objectives compared to the straight lines with no or limited HRC.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Industry 4.0, assembly line balancing, scheduling, human-robot collaboration, line layout, mathematical model
National Category
Robotics Production Engineering, Human Work Science and Ergonomics
Research subject
VF-KDO; Virtual Production Development (VPD)
Identifiers
urn:nbn:se:his:diva-23413 (URN)10.1016/j.cie.2023.109775 (DOI)001135405700001 ()2-s2.0-85179002846 (Scopus ID)
Funder
VinnovaKnowledge Foundation
Note

CC BY 4.0 DEED

Corresponding author: Email: amir.nourmohammadi@his.se

This study was funded by the Knowledge Foundation (KKS) and Sweden’s Innovation Agency through the VF-KDO, ACCURATE 4.0, and PREFER projects.

Available from: 2023-12-04 Created: 2023-12-04 Last updated: 2024-01-19Bibliographically approved
Mahmoodi, E., Fathi, M., Tavana, M., Ghobakhloo, M. & Ng, A. H. C. (2024). Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing. Journal of manufacturing systems, 72, 287-307
Open this publication in new window or tab >>Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing
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2024 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 72, p. 287-307Article in journal (Refereed) Published
Abstract [en]

Data-driven simulation (DDS) is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of DDS for resource allocation (RA) in high-mix, low-volume smart manufacturing systems with mixed automation levels. A DDS-based decision support system (DDS-DSS) is developed by incorporating two RA strategies: simulation-based bottleneck analysis (SB-BA) and simulation-based multi-objective optimization (SB-MOO). To enhance the performance of SB-MOO, a unique meta-learning mechanism featuring memory, dynamic orthogonal array, and learning rate is integrated into the NSGA-II, resulting in a modified version of the NSGA-II with meta-learning (i.e., NSGA-II-ML). The proposed DSS also benefits from a post-optimality analysis that leverages a clustering algorithm to derive actionable insights. A real-life marine engine manufacturing application study is presented to demonstrate the applicability and exhibit efficacy of the proposed DSS and NSGA-II-ML. To this aim, NSGA-II-ML was tested against the original NSGA-II and differential evolution (DE) algorithm across a set of test problems. The results revealed that NSGA-II-ML surpassed the other two in terms of the number of non-dominated solutions and hypervolume, particularly in medium and large-sized problems. Furthermore, NSGA-II-ML achieved a 24% improvement in the best throughput found in the real case problem, outperforming SB-BA, NSGA-II, and DE. The post-optimality analysis led to the extraction of valuable knowledge about the key, influencing decision variables on the throughput.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Resource allocation, High-mix low-volume, Multi-objective optimization, Data-driven simulation, Decision support system, Industry 4.0, Meta-learning
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Virtual Production Development (VPD)
Identifiers
urn:nbn:se:his:diva-23465 (URN)10.1016/j.jmsy.2023.11.019 (DOI)001140004800001 ()2-s2.0-85183766753 (Scopus ID)
Projects
ACCURATE 4.0PREFER
Funder
Knowledge FoundationVinnova
Note

CC BY 4.0 DEED

Corresponding author at: Division of Intelligent Production Systems, School of Engineering Science, University of Skövde, 54128 Skövde, Sweden. E-mail address: masood.fathi@his.se (M. Fathi).

This study was funded by the Knowledge Foundation (KKS) and Sweden’s Innovation Agency via the ACCURATE 4.0 (grant agreement No. 20200181) and PREFER projects, respectively.

Available from: 2023-12-13 Created: 2023-12-13 Last updated: 2024-02-15Bibliographically approved
Kumbhar, M., Ng, A. H. C. & Bandaru, S. (2023). A digital twin based framework for detection, diagnosis, and improvement of throughput bottlenecks. Journal of manufacturing systems, 66, 92-106
Open this publication in new window or tab >>A digital twin based framework for detection, diagnosis, and improvement of throughput bottlenecks
2023 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 66, p. 92-106Article in journal (Refereed) Published
Abstract [en]

Digitalization through Industry 4.0 technologies is one of the essential steps for the complete collaboration, communication, and integration of heterogeneous resources in a manufacturing organization towards improving manufacturing performance. One of the ways is to measure the effective utilization of critical resources, also known as bottlenecks. Finding such critical resources in a manufacturing system has been a significant focus of manufacturing research for several decades. However, finding a bottleneck in a complex manufacturing system is difficult due to the interdependencies and interactions of many resources. In this work, a digital twin framework is developed to detect, diagnose, and improve bottleneck resources using utilization-based bottleneck analysis, process mining, and diagnostic analytics. Unlike existing bottleneck detection methods, this novel approach is capable of directly utilizing enterprise data from multiple levels, namely production planning, process execution, and asset monitoring, to generate event-log which can be fed into a digital twin. This enables not only the detection and diagnosis of bottleneck resources, but also validation of various what-if improvement scenarios. The digital twin itself is generated through process mining techniques, which can extract the main process map from a complex system. The results show that the utilization can detect both sole and shifting bottlenecks in a complex manufacturing system. Diagnosing and managing bottleneck resources through the proposed approach yielded a minimum throughput improvement of 10% in a real factory setting. The concept of a custom digital twin for a specific context and goal opens many new possibilities for studying the strong interaction of multi-source data and decision-making in a manufacturing system. This methodology also has the potential to be exploited for multi-objective optimization of bottleneck resources.

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Digital twin, Bottleneck detection, Process mining, Factory physics, Utilization, Simulation, Industry 4.0
National Category
Production Engineering, Human Work Science and Ergonomics Information Systems
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-22140 (URN)10.1016/j.jmsy.2022.11.016 (DOI)000905124700001 ()2-s2.0-85143881517 (Scopus ID)
Funder
Knowledge Foundation, 20200011
Note

CC BY 4.0

E-mail addresses:mahesh.kumbhar@his.se (M. Kumbhar) [Corresponding author], amos.ng@his.se, amos.ng@angstrom.uu.se (A.H.C. Ng), sunith.bandaru@his.se (S. Bandaru).

The authors acknowledge the financial support received from KK-stiftelsen (The Knowledge Foundation, Stockholm, Sweden) for the research project ‘TOPAZ - Towards Prescriptive Analytics in Virtual Factories through Structured Data Mining and Optimization’ under grant 20200011.

Available from: 2022-12-20 Created: 2022-12-20 Last updated: 2023-01-19Bibliographically approved
Barrera Diaz, C. A., Nourmohammadi, A., Smedberg, H., Aslam, T. & Ng, A. H. C. (2023). An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems. Mathematics, 11(6), Article ID 1527.
Open this publication in new window or tab >>An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems
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2023 (English)In: Mathematics, ISSN 2227-7390, Vol. 11, no 6, article id 1527Article in journal (Refereed) Published
Abstract [en]

In today’s uncertain and competitive market, where manufacturing enterprises are subjected to increasingly shortened product lifecycles and frequent volume changes, reconfigurable manufacturing system (RMS) applications play significant roles in the success of the manufacturing industry. Despite the advantages offered by RMSs, achieving high efficiency constitutes a challenging task for stakeholders and decision makers when they face the trade-off decisions inherent in these complex systems. This study addresses work task and resource allocations to workstations together with buffer capacity allocation in an RMS. The aim is to simultaneously maximize throughput and to minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach supports decision makers with knowledge discovery to further understand RMS design. In particular, this study presents a customized SMO approach combined with a novel flexible pattern mining method for optimizing an RMS and conducts post-optimal analyses. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision support and production planning of an RMS.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
reconfigurable manufacturing system, simulation, multi-objective optimization, knowledge discovery
National Category
Production Engineering, Human Work Science and Ergonomics Computer Sciences
Research subject
Virtual Production Development (VPD); VF-KDO
Identifiers
urn:nbn:se:his:diva-22329 (URN)10.3390/math11061527 (DOI)000960178700001 ()2-s2.0-85151391170 (Scopus ID)
Funder
Knowledge Foundation, 2018-0011
Note

CC BY 4.0

(This article belongs to the Special Issue Multi-Objective Optimization and Decision Support Systems)

Received: 15 February 2023 / Revised: 15 March 2023 / Accepted: 17 March 2023 / Published: 21 March 2023

The authors thank the Knowledge Foundation, Sweden (KKS) for funding this research through the KKS Profile Virtual Factories with Knowledge-Driven Optimization, VF-KDO, grant number 2018-0011.

Available from: 2023-03-21 Created: 2023-03-21 Last updated: 2023-09-01Bibliographically approved
Flores-García, E., Barrera Diaz, C. A., Wiktorsson, M., Ng, A. H. C. & Aslam, T. (2023). Enabling CPS and simulation-based multi-objective optimisation for material handling of reconfigurable manufacturing systems. The International Journal of Advanced Manufacturing Technology
Open this publication in new window or tab >>Enabling CPS and simulation-based multi-objective optimisation for material handling of reconfigurable manufacturing systems
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2023 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015Article in journal (Other academic) Submitted
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Virtual Production Development (VPD)
Identifiers
urn:nbn:se:his:diva-23114 (URN)
Funder
Vinnova
Note

The authors would like to acknowledge the support of Swedish Innovation Agency (VINNOVA) and its funding program, Produktion2030. This study is part of the Explainable and Learning Production Logistics by Artificial Intelligence (EXPLAIN) project.

Available from: 2023-08-18 Created: 2023-08-18 Last updated: 2023-09-11Bibliographically approved
Smedberg, H., Bandaru, S., Riveiro, M. & Ng, A. H. C. (2023). Mimer: A web-based tool for knowledge discovery in multi-criteria decision support. IEEE Computational Intelligence Magazine
Open this publication in new window or tab >>Mimer: A web-based tool for knowledge discovery in multi-criteria decision support
2023 (English)In: IEEE Computational Intelligence Magazine, ISSN 1556-603X, E-ISSN 1556-6048Article in journal (Other academic) Submitted
National Category
Computer Sciences Information Systems Software Engineering Computer Systems Computational Mathematics
Research subject
Virtual Production Development (VPD); Skövde Artificial Intelligence Lab (SAIL); VF-KDO
Identifiers
urn:nbn:se:his:diva-23154 (URN)
Funder
Knowledge Foundation, 2018-0011
Note

This work was funded by the Knowledge Foundation (KKS), Sweden, through the KKS Profile, Virtual Factories with Knowledge-Driven Optimization (VF-KDO), Grant No. 2018-0011.

Available from: 2023-09-01 Created: 2023-09-01 Last updated: 2023-09-11Bibliographically approved
Nourmohammadi, A., Ng, A. H. C., Fathi, M., Vollebregt, J. & Hanson, L. (2023). Multi-objective optimization of mixed-model assembly lines incorporating musculoskeletal risks assessment using digital human modeling. CIRP - Journal of Manufacturing Science and Technology, 47, 71-85
Open this publication in new window or tab >>Multi-objective optimization of mixed-model assembly lines incorporating musculoskeletal risks assessment using digital human modeling
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2023 (English)In: CIRP - Journal of Manufacturing Science and Technology, ISSN 1755-5817, E-ISSN 1878-0016, Vol. 47, p. 71-85Article in journal (Refereed) Published
Abstract [en]

In line with Industry 5.0, ergonomic factors have recently received more attention in balancing assembly lines to enhance the human-centric aspect. Meanwhile, today’s mass-customized trend yields manufacturers to offset the assembly lines for different product variants. Thus, this study addresses the mixed-model assembly line balancing problem (MMALBP) by considering worker posture. Digital human modeling and posture assessment technologies are utilized to assess the risks of work-related musculoskeletal disorders using a method known as rapid entire body analysis (REBA). The resulting MMALBP is formulated as a mixed-integer linear programming (MILP) model while considering three objectives: cycle time, maximum ergonomic risk of workstations, and total ergonomic risks. An enhanced non-dominated sorting genetic algorithm (E-NSGA-II) is developed by incorporating a local search procedure that generates neighborhood solutions and a multi-criteria decision-making mechanism that ensures the selection of promising solutions. The E-NSGA-II is benchmarked against Epsilon-constraint, MOGA, and NSGA-II while solving a case study and also test problems taken from the literature. The computational results show that E-NSGA-II can find promising Pareto front solutions while dominating the considered methods in terms of performance metrics. The robustness of E-NSGA-II results is evaluated through one-way ANOVA statistical tests. The analysis of results shows that a smooth distribution of time and ergonomic loads among the workstations can be achieved when all three objectives are simultaneously considered.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Mixed-model assembly line balancing, Digital human modeling, Musculoskeletal risks, Multi-objective optimization, Mathematical mode, lNSGA-II
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Virtual Production Development (VPD); User Centred Product Design; VF-KDO
Identifiers
urn:nbn:se:his:diva-23240 (URN)10.1016/j.cirpj.2023.09.002 (DOI)001082058800001 ()2-s2.0-85171985258 (Scopus ID)
Funder
Knowledge Foundation
Note

CC BY 4.0

Corresponding author. E-mail address: amir.nourmohammadi@his.se (A. Nourmohammadi).

This study is supported by the Knowledge Foundation (KKS) and Sweden’s Innovation Agency through the VF-KDO, ACCURATE 4.0, and PREFER Projects. The authors highly appreciate the valuable collaborations with the experts from the industrial partner of this research.

Available from: 2023-09-22 Created: 2023-09-22 Last updated: 2023-11-06Bibliographically approved
Lidberg, S. & Ng, A. (2023). Reproducible decision support for industrial decision making using a knowledge extraction platform on multi-objective optimization data. International Journal of Manufacturing Research, 18(4), 454-480
Open this publication in new window or tab >>Reproducible decision support for industrial decision making using a knowledge extraction platform on multi-objective optimization data
2023 (English)In: International Journal of Manufacturing Research, ISSN 1750-0591, Vol. 18, no 4, p. 454-480Article in journal (Refereed) Published
Abstract [en]

Simulation-based optimisation enables companies to take decisions based on data, and allows prescriptive analysis of current and future production scenarios, creating a competitive edge. However, effectively visualising and extracting knowledge from the vast amounts of data generated by many-objective optimisation algorithms can be challenging. We present an open-source, web-based application in the R language to extract knowledge from data generated from simulation-based optimisation. For the tool to be useful for real-world industrial decision-making support, several decision makers gave their requirements for such a tool. This information was used to augment the tool to provide the desired features for decision support in the industry. The open-source tool is then used to extract knowledge from two industrial use cases. Furthermore, we discuss future work, including planned additions to the open-source tool and the exploration of automatic model generation.

Place, publisher, year, edition, pages
InderScience Publishers, 2023
Keywords
knowledge-extraction, reproducible science, simulation-based optimisation, industrial use-case, decision-support, knowledge-driven optimisation
National Category
Production Engineering, Human Work Science and Ergonomics Computer Sciences Software Engineering
Research subject
VF-KDO; Virtual Production Development (VPD)
Identifiers
urn:nbn:se:his:diva-23078 (URN)10.1504/IJMR.2023.135645 (DOI)001128775300001 ()2-s2.0-85180929057 (Scopus ID)
Funder
Knowledge Foundation
Note

CC BY 4.0

Alternativ/tidigare DOI: 10.1504/ijmr.2024.10057049

Available from: 2023-08-09 Created: 2023-08-09 Last updated: 2024-02-14Bibliographically approved
Holm, M., Ng, A. H. C., Högberg, D. & Syberfeldt, A. (Eds.). (2023). Special Issue: Digital Transformation Towards a Sustainable Human Centric and Resilient Production. Paper presented at Swedish Production Symposium 2022. InderScience Publishers
Open this publication in new window or tab >>Special Issue: Digital Transformation Towards a Sustainable Human Centric and Resilient Production
2023 (English)Collection (editor) (Refereed)
Abstract [en]

The realisation of a successful product requires collaboration between developers andproducers, taking account of stakeholder value, reinforcing the contribution of industry tosociety and enhancing the wellbeing of workers while respecting planetary boundaries.Founded in 2006, the Swedish Production Academy (SPA) aims to drive and developproduction research and education and to increase cooperation within the production area.SPA initiated and hosts the conference Swedish Production Symposium. This specialissue is based on invited papers from the 10th Swedish Production Symposium(SPS2022), held in Skövde, Sweden, from 26–29 April 2022. The overall theme forSPS2022 was ‘Industry 5.0 transformation – towards a sustainable, human-centric, andresilient production’.As stated by the European Commission the vision of Industry 5.0 recognises societalgoals. It goes beyond a techno-economic vision, industrial value chains and growthaiming for the industry to become a resilient provider of prosperity, respecting ourplanets boundaries, and placing the industrial worker, her well-being, at the centre of theproduction process.In this special issue, we set out to explore the transition to a resilient, sustainable andhuman centric industry. The first paper explores the need for a joint strategical vision thatinclude technology (selection, development, and implementation), organisation(structure, agility, management, stakeholder collaborations, work environment) andpeople (skills and competences, participation, innovation and creative collaborativeculture, and change readiness), to achieve a resilient and sustainable production systemeffectively and efficiently. The second paper discusses how reconfigurable manufacturingsystems can enable sustainable manufacturing and circularity, achieving highresponsiveness and cost efficiency. The third paper, a synthesis of universal workplacedesign in assembly, explores how human assembly workplaces can be designed in abetter way in regard to inclusion of diverse worker populations. The fourth paperdiscusses different meanings of digital transformation in manufacturing industry fromboth a theoretical and industrial perspective. The fifth paper explores challenges to designa product service system at an SME as an approach to support transition to Industry 5.0.The concluding paper in this special issue discusses a knowledge extraction platform forreproducible decision support based on data from multi-objective experiments.The organiser of SPS2022 has found these six outstanding papers to perfectly alignwith the theme ‘Industry 5.0 transformation’ and express their gratitude to theEditor-in-Chief of IJMR for accepting them for publication in this special issue.

Place, publisher, year, edition, pages
InderScience Publishers, 2023
Series
International Journal of Manufacturing Research, ISSN 1750-0591, E-ISSN 1750-0605 ; Vol. 18(4)
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
VF-KDO; User Centred Product Design; Virtual Production Development (VPD)
Identifiers
urn:nbn:se:his:diva-23455 (URN)
Conference
Swedish Production Symposium 2022
Available from: 2023-12-12 Created: 2023-12-12 Last updated: 2023-12-27Bibliographically approved
Nourmohammadi, A., Fathi, M., Ng, A. H. C. & Mahmoodi, E. (2022). A genetic algorithm for heterogenous human-robot collaboration assembly line balancing problems. Paper presented at 55th CIRP Conference on Manufacturing Systems. Procedia CIRP, 107, 1444-1448
Open this publication in new window or tab >>A genetic algorithm for heterogenous human-robot collaboration assembly line balancing problems
2022 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 107, p. 1444-1448Article in journal (Refereed) Published
Abstract [en]

Originated by a real-world case study from the automotive industry, this paper attempts to address the assembly lines balancing problem with human-robot collaboration and heterogeneous operators while optimizing the cycle time. A genetic algorithm (GA) with customized parameters and features is proposed while considering the characteristics of the problem. The computational results show that the developed GA can provide the decision-makers with efficient solutions with heterogeneous humans and robots. Furthermore, the results reveal that the cycle time is highly influenced by order of the operators’ skills, particularly when a fewer number of humans and robots exist at the stations.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Human-robot collaboration, assembly line balancing, genetic algorithms
National Category
Engineering and Technology Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-21178 (URN)10.1016/j.procir.2022.05.172 (DOI)2-s2.0-85132304719 (Scopus ID)
Conference
55th CIRP Conference on Manufacturing Systems
Projects
VF-KDOACCURATE 4.0
Funder
Knowledge Foundation
Note

CC BY-NC-ND 4.0

Corresponding author: Amir Nourmohammadi

Edited by Emanuele Carpanzano, Claudio Boër, Anna Valente

This study is funded by the Knowledge Foundation (KKS), Sweden, through the VF-KDO and ACCURATE 4.0 projects at the University of Skövde, Sweden.

Available from: 2022-05-27 Created: 2022-05-27 Last updated: 2023-05-02Bibliographically approved
Projects
Holistic Simulation Optimisation for Sustainable and Profitable Production [2009-01592_Vinnova]; University of SkövdeVirtual factories with knowledge-driven optimization (VF-KDO); University of Skövde; Publications
Nourmohammadi, A., Fathi, M. & Ng, A. H. C. (2024). Balancing and scheduling human-robot collaborated assembly lines with layout and objective consideration. Computers & industrial engineering, 187, Article ID 109775. Lind, A., Iriondo Pascual, A., Hanson, L., Högberg, D., Lämkull, D. & Syberfeldt, A. (2024). Multi-objective optimisation of a logistics area in the context of factory layout planning. Production & Manufacturing Research, 12(1), Article ID 2323484. Barrera Diaz, C. A., Nourmohammadi, A., Smedberg, H., Aslam, T. & Ng, A. H. C. (2023). An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems. Mathematics, 11(6), Article ID 1527. Lind, A., Hanson, L., Högberg, D., Lämkull, D., Mårtensson, P. & Syberfeldt, A. (2023). Digital support for rules and regulations when planning and designing factory layouts. Procedia CIRP, 120, 1445-1450Redondo Verdú, C., Sempere Maciá, N., Strand, M., Holm, M., Schmidt, B. & Olsson, J. (2023). Enhancing Manual Assembly Training using Mixed Reality and Virtual Sensors. Paper presented at 17th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME '23, Gulf of Naples, Italy, 12 - 14 July 2023. Procedia CIRPLind, A., Hanson, L., Högberg, D., Lämkull, D. & Syberfeldt, A. (2023). Extending and demonstrating an engineering communication framework utilising the digital twin concept in a context of factory layouts. International Journal of Services Operations and Informatics, 12(3), 201-224Danielsson, O., Syberfeldt, A., Holm, M. & Thorvald, P. (2023). Integration of Augmented Reality Smart Glasses as Assembly Support: A Framework Implementation in a Quick Evaluation Tool. International Journal of Manufacturing Research, 18(2), 144-164Smedberg, H. & Bandaru, S. (2023). Interactive knowledge discovery and knowledge visualization for decision support in multi-objective optimization. European Journal of Operational Research, 306(3), 1311-1329Smedberg, H. (2023). Knowledge discovery for interactive decision support and knowledge-driven optimization. (Doctoral dissertation). Skövde: University of SkövdeSmedberg, H., Bandaru, S., Riveiro, M. & Ng, A. H. C. (2023). Mimer: A web-based tool for knowledge discovery in multi-criteria decision support. IEEE Computational Intelligence Magazine
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ORCID iD: ORCID iD iconorcid.org/0000-0003-0111-1776

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