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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
Pour, P. A., Bandaru, S., Afsar, B., Emmerich, M. & Miettinen, K. (2023). A Performance Indicator for Interactive Evolutionary Multiobjective Optimization Methods. IEEE Transactions on Evolutionary Computation
Open this publication in new window or tab >>A Performance Indicator for Interactive Evolutionary Multiobjective Optimization Methods
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2023 (English)In: IEEE Transactions on Evolutionary Computation, ISSN 1089-778X, E-ISSN 1941-0026Article in journal (Refereed) Epub ahead of print
Abstract [en]

In recent years, interactive evolutionary multiobjective optimization methods have been getting more and more attention. In these methods, a decision maker, who is a domain expert, is iteratively involved in the solution process and guides the solution process toward her/his desired region with preference information. However, there have not been many studies regarding the performance evaluation of interactive evolutionary methods. On the other hand, indicators have been developed for a priori methods, where the DM provides preference information before optimization. In the literature, some studies treat interactive evolutionary methods as a series of a priori steps when assessing and comparing them. In such settings, indicators designed for a priori methods can be utilized. In this paper, we propose a novel performance indicator for interactive evolutionary multiobjective optimization methods and show how it can assess the performance of these interactive methods as a whole process and not as a series of separate steps. In addition, we demonstrate the shortcomings of using indicators designed for a priori methods for comparing interactive evolutionary methods. IEEE

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Convergence, decision making, hypervolume indicator, interactive evolutionary algorithms, Linear programming, method comparison, Pareto optimization, Quality indicators, Space exploration, Switches, Task analysis, Terminology, Benchmarking, Iterative methods, Job analysis, Multiobjective optimization, Pareto principle, Quality control, Space research, Decisions makings, Hypervolume indicators, Linear-programming, Pareto-optimization, Space explorations
National Category
Other Engineering and Technologies not elsewhere specified Other Computer and Information Science Computer Sciences
Research subject
Virtual Production Development (VPD)
Identifiers
urn:nbn:se:his:diva-22632 (URN)10.1109/TEVC.2023.3272953 (DOI)2-s2.0-85159829087 (Scopus ID)
Funder
Academy of Finland, 322221
Note

CC BY 4.0

This research was partly supported by the Academy of Finland (Grant No. 322221) and is related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization, jyu.fi/demo) of the University of Jyväskylä.

Available from: 2023-06-01 Created: 2023-06-01 Last updated: 2023-11-24Bibliographically approved
Smedberg, H. & Bandaru, S. (2023). Interactive knowledge discovery and knowledge visualization for decision support in multi-objective optimization. European Journal of Operational Research, 306(3), 1311-1329
Open this publication in new window or tab >>Interactive knowledge discovery and knowledge visualization for decision support in multi-objective optimization
2023 (English)In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 306, no 3, p. 1311-1329Article in journal (Refereed) Published
Abstract [en]

In many practical applications, the end-goal of multi-objective optimization is to select an implementable solution that is close to the Pareto-optimal front while satisfying the decision maker’s preferences. The decision making process is challenging since it involves the manual consideration of all solutions. The field of multi-criteria decision making offers many methods that help the decision maker in this process. However, most methods only focus on analyzing the solutions’ objective values. A more informed decision generally requires the additional knowledge of how different preferences affect the variable values. One difficulty in realizing this is that while the preferences are often expressed in the objective space, the knowledge required to implement a preferred solution exists in the decision space. In this paper, we propose a decision support system that allows interactive knowledge discovery and knowledge visualization to support practitioners by simultaneously considering preferences in the objective space and their impact in the decision space. The knowledge discovery step can use either of two recently proposed data mining techniques for extracting decision rules that conform to given preferences, while the extracted knowledge is visualized via a novel graph-based approach that allows the discovery of important variables, their values and their interactions with other variables. The result is an intuitive and interactive decision support system that aids the entire decision making process — from solution visualization to knowledge visualization. We demonstrate the usefulness of this system on benchmark optimization problems up to 10 objectives and real-world problems with up to six objectives.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Decision support systems, Multi-objective optimization, Multiple criteria decision making, Data mining, Knowledge discovery
National Category
Computer Sciences Other Computer and Information Science Computer Systems Information Systems
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-21874 (URN)10.1016/j.ejor.2022.09.008 (DOI)000925146600001 ()2-s2.0-85139046739 (Scopus ID)
Funder
Knowledge Foundation
Note

CC BY 4.0

Corresponding author: Henrik Smedberg. E-mail addresses: henrik.smedberg@his.se (H. Smedberg), sunith.bandaru@his.se (S. Bandaru).

Erratum in: European Journal of Operational Research, Volume 308, Issue 1, 2023, Pages 496-497. doi:10.1016/j.ejor.2023.01.040

The authors acknowledge the financial support received from KK-stiftelsen (The Knowledge Foundation, Stockholm, Sweden) under the Research Profile 2018 project Virtual Factories with Knowledge-Driven Optimization. For more information, please visit www.virtualfactories.se/

Available from: 2022-09-28 Created: 2022-09-28 Last updated: 2023-09-01Bibliographically 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
Smedberg, H. & Bandaru, S. (2022). A Modular Knowledge-Driven Mutation Operator for Reference-Point Based Evolutionary Algorithms. In: IEEE Congress of Evolutionary Computation, CEC - Conference Proceedings: . Paper presented at 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings CEC 2022 Padua 18 July 2022 through 23 July 2022. IEEE
Open this publication in new window or tab >>A Modular Knowledge-Driven Mutation Operator for Reference-Point Based Evolutionary Algorithms
2022 (English)In: IEEE Congress of Evolutionary Computation, CEC - Conference Proceedings, IEEE, 2022Conference paper, Published paper (Refereed)
Abstract [en]

Although an entire frontier of Pareto-optimal solutions exists for multi-objective optimization problems, in practice, decision makers are often only interested in a small subset of these solutions, called the region of interest. Specialized optimizers, such as reference-point based evolutionary algorithms, exist that can focus the search to only find solutions inside this region of interest. These algorithms typically only modify the selection mechanism of regular multi-objective optimizers to preferentially select solutions that conform to the reference point. However, a more effective search may be performed by additionally modifying the variation mechanism of the optimizers, namely the crossover and the mutation operators, to preferentially generate solutions conforming to the reference point. In this paper, we propose a modular mutation operator that uses a recent knowledge discovery technique to first find decision rules unique to the preferred solutions in each generation. These rules are then used to build an empirical distribution in the decision space that can be sampled to generate new mutated solutions which are more likely to be closer to the preferred solutions. The operator is modular in the sense that it can be used with any existing reference-point based evolutionary algorithm by simply replacing the mutation operator. We incorporate the proposed knowledge-driven mutation operator into three such algorithms, and through benchmark test problems up to 10 objectives, demonstrate that their performance improves significantly in the majority of cases according to two different performance indicators. 

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Benchmarking, Decision making, Genetic algorithms, Image segmentation, Pareto principle, Modular knowledge, Modulars, Multi-objectives optimization, Mutation operators, Optimizers, Point-based, Preferred solutions, Reference points, Region-of-interest, Regions of interest, Multiobjective optimization, knowledge discovery, multi-objective optimization, mutation operator, reference point
National Category
Computer Sciences Computational Mathematics Other Computer and Information Science
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-21917 (URN)10.1109/CEC55065.2022.9870268 (DOI)000859282000053 ()2-s2.0-85138691252 (Scopus ID)978-1-6654-6708-7 (ISBN)978-1-6654-6709-4 (ISBN)
Conference
2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings CEC 2022 Padua 18 July 2022 through 23 July 2022
Note

© 2022 IEEE.

Available from: 2022-10-06 Created: 2022-10-06 Last updated: 2023-09-01Bibliographically approved
Kumbhar, M., Ng, A. H. C. & Bandaru, S. (2022). Bottleneck Detection Through Data Integration, Process Mining and Factory Physics-Based Analytics. In: Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm (Ed.), SPS2022: Proceedings of the 10th Swedish Production Symposium. Paper presented at 10th Swedish Production Symposium (SPS2022), Skövde, April 26–29 2022 (pp. 737-748). Amsterdam; Berlin; Washington, DC: IOS Press
Open this publication in new window or tab >>Bottleneck Detection Through Data Integration, Process Mining and Factory Physics-Based Analytics
2022 (English)In: SPS2022: Proceedings of the 10th Swedish Production Symposium / [ed] Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm, Amsterdam; Berlin; Washington, DC: IOS Press, 2022, p. 737-748Conference paper, Published paper (Refereed)
Abstract [en]

Production systems are evolving rapidly, thanks to key Industry 4.0 technologies such as production simulation, digital twins, internet-of-things, artificial intelligence, and big data analytics. The combination of these technologies can be used to meet the long-term enterprise goals of profitability, sustainability, and stability by increasing the throughput and reducing production costs. Owing to digitization, manufacturing companies can now explore operational level data to track the performance of systems making processes more transparent and efficient. This untapped granular data can be leveraged to better understand the system and identify constraining activities or resources that determine the system’s throughput. In this paper, we propose a data-driven methodology that exploits the technique of data integration, process mining, and analytics based on factory physics to identify constrained resources, also known as bottlenecks. To test the proposed methodology, a case study was performed on an industrial scenario were a discrete event simulation model is built and validated to run future what-if analyses and optimization scenarios. The proposed methodology is easy to implement and can be generalized to any other organization that captures event data.

Place, publisher, year, edition, pages
Amsterdam; Berlin; Washington, DC: IOS Press, 2022
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 21
Keywords
Process Mining, Factory Physics, Data Analytics, Manufacturing, Bottleneck Detection
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-21114 (URN)10.3233/ATDE220192 (DOI)2-s2.0-85132820944 (Scopus ID)978-1-64368-268-6 (ISBN)978-1-64368-269-3 (ISBN)
Conference
10th Swedish Production Symposium (SPS2022), Skövde, April 26–29 2022
Note

CC BY-NC 4.0

Corresponding Author: Mahesh Kumbhar, School of Engineering Science, University of Skövde, Skövde, Sweden; E-mail: mahesh.kumbhar@his.se

Available from: 2022-05-04 Created: 2022-05-04 Last updated: 2023-06-16Bibliographically approved
Pour, P. A., Bandaru, S., Afsar, B. & Miettinen, K. (2022). Desirable properties of performance indicators for assessing interactive evolutionary multiobjective optimization methods. In: Jonathan E. Fieldsend; Markus Wagner (Ed.), GECCO '22: Proceedings of the 2022 Genetic and Evolutionary Computation Conference. Paper presented at 2022 Genetic and Evolutionary Computation Conference, GECCO '22, July 9-13, 2022, Boston, Massachusetts (pp. 1803-1811). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Desirable properties of performance indicators for assessing interactive evolutionary multiobjective optimization methods
2022 (English)In: GECCO '22: Proceedings of the 2022 Genetic and Evolutionary Computation Conference / [ed] Jonathan E. Fieldsend; Markus Wagner, Association for Computing Machinery (ACM), 2022, p. 1803-1811Conference paper, Published paper (Refereed)
Abstract [en]

Interactive methods support decision makers in finding the most preferred solution in multiobjective optimization problems. They iteratively incorporate the decision maker's preference information to find the best balance among conflicting objectives. Several interactive methods have been developed in the literature. However, choosing the most suitable interactive method for a given problem can prove challenging and appropriate indicators are needed to compare interactive methods. Some indicators exist for a priori methods, where preferences are provided at the beginning of the solution process. We present some numerical experiments that illustrate why these indicators are not suitable for interactive methods. As the main contribution of this paper, we propose a set of desirable properties of indicators for assessing interactive methods as the first step of filling a gap in the literature. We discuss each property in detail and provide simple examples to illustrate their behavior. 

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2022
Keywords
Decision making, Multiobjective optimization, Numerical methods, Decision makers, Evolutionary multiobjective optimization, Interactive methods, Multiple-criterion optimization, Optimization method, Performance assessment, Performance indicators, Performances evaluation, Preferred solutions, Property, Iterative methods, multiple criteria optimization, performance evaluation
National Category
Computer Sciences Information Systems Interaction Technologies
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-21743 (URN)10.1145/3520304.3533955 (DOI)001035469400282 ()2-s2.0-85136330864 (Scopus ID)978-1-4503-9268-6 (ISBN)
Conference
2022 Genetic and Evolutionary Computation Conference, GECCO '22, July 9-13, 2022, Boston, Massachusetts
Note

© 2022 ACM

Available from: 2022-09-01 Created: 2022-09-01 Last updated: 2023-09-08Bibliographically approved
Frantzén, M., Bandaru, S. & Ng, A. H. C. (2022). Digital-twin-based decision support of dynamic maintenance task prioritization using simulation-based optimization and genetic programming. Decision Analytics Journal, 3, Article ID 100039.
Open this publication in new window or tab >>Digital-twin-based decision support of dynamic maintenance task prioritization using simulation-based optimization and genetic programming
2022 (English)In: Decision Analytics Journal, E-ISSN 2772-6622, Vol. 3, article id 100039Article in journal (Refereed) Published
Abstract [en]

Modern decision support systems need to be connected online to equipment so that the large amount of data available can be used to guide the decisions of shop floor operators, making full use of the potential of industrial manufacturing systems. This paper investigates a novel optimization and data analytic method to implement such a decision support system, based on heuristic generation using genetic programming and simulation-based optimization running on a digital twin. Such a digital-twin-based decision support system allows the proactively searching of the best attribute combinations to be used in a data-driven composite dispatching rule for the short-term corrective maintenance task prioritization. Both the job (e.g., bottlenecks) and operator priorities use multiple criteria, including competence, utilization, operator walking distances on the shop floor, bottlenecks, work-in-process, and parallel resource availability. The data-driven composite dispatching rules are evaluated using a digital twin, built for a real-world machining line, which simulates the effects of decisions regarding disruptions. Experimental results show improved productivity because of using the composite dispatching rules generated by such heuristic generation method compared to the priority dispatching rules based on similar attributes and methods. The improvement is more pronounced when the number of operators is reduced. This paper thus offers new insights about how shop floor data can be transformed into useful knowledge with a digital-twin-based decision support system to enhance resource efficiency.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Decision support systems, Digital Twin, Short-term corrective maintenance priority, Genetic programming, Simulation-based optimization, Bottleneck
National Category
Production Engineering, Human Work Science and Ergonomics Computer Sciences
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-22294 (URN)10.1016/j.dajour.2022.100039 (DOI)
Note

CC BY-NC-ND 4.0

Received 30 December 2021, Revised 9 March 2022, Accepted 15 March 2022, Available online 18 March 2022, Version of Record 2 April 2022.

The co-authors would like to acknowledge the Knowledge Foundation (KKS), Sweden, for their funding through the research profile Virtual Factories with Knowledge-Driven Optimization at the University of Skövde which is partially related to this work.

Available from: 2023-02-22 Created: 2023-02-22 Last updated: 2023-11-24Bibliographically approved
Barrera Diaz, C. A., Smedberg, H., Bandaru, S. & Ng, A. H. C. (2022). Enabling Knowledge Discovery from Simulation-Based Multi-Objective Optimization in Reconfigurable Manufacturing Systems. In: B. Feng; G. Pedrielli; Y. Peng; S. Shashaani; E. Song; C. G. Corlu; L. H. Lee; E. P. Chew; T. Roeder; P. Lendermann (Ed.), Proceedings of the 2022 Winter Simulation Conference: . Paper presented at 2022 Winter Simulation Conference, W.S.C. 2022 Guilin 11 December 2022 through 14 December 2022 (pp. 1794-1805). IEEE
Open this publication in new window or tab >>Enabling Knowledge Discovery from Simulation-Based Multi-Objective Optimization in Reconfigurable Manufacturing Systems
2022 (English)In: Proceedings of the 2022 Winter Simulation Conference / [ed] B. Feng; G. Pedrielli; Y. Peng; S. Shashaani; E. Song; C. G. Corlu; L. H. Lee; E. P. Chew; T. Roeder; P. Lendermann, IEEE, 2022, p. 1794-1805Conference paper, Published paper (Refereed)
Abstract [en]

Due to the nature of today's manufacturing industry, where enterprises are subjected to frequent changes and volatile markets, reconfigurable manufacturing systems (RMS) are crucial when addressing ramp-up and ramp-down scenarios derived from, among other challenges, increasingly shortened product lifecycles. Applying simulation-based optimization techniques to their designs under different production volume scenarios has become valuable when RMS becomes more complex. Apart from proposing the optimal solutions subject to various production volume changes, decision-makers can extract propositional knowledge to better understand the RMS design and support their decision-making through a knowledge discovery method by combining simulation-based optimization and data mining techniques. In particular, this study applies a novel flexible pattern mining algorithm to conduct post-optimality analysis on multi-dimensional, multi-objective optimization datasets from an industrial-inspired application to discover the rules regarding how the tasks are assigned to the workstations constitute reasonable solutions for scalable RMS. 

Place, publisher, year, edition, pages
IEEE, 2022
Series
Proceedings of the Winter Simulation Conference, ISSN 0891-7736, E-ISSN 1558-4305
Keywords
Computer aided manufacturing, Data mining, Decision making, Life cycle, Enterprise IS, Manufacturing industries, Multi-objectives optimization, Optimization techniques, Product life cycles, Production volumes, Ramp up, Reconfigurable manufacturing system, Simulation-based optimizations, Volatile markets, Multiobjective optimization
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Virtual Production Development (VPD); VF-KDO
Identifiers
urn:nbn:se:his:diva-22271 (URN)10.1109/WSC57314.2022.10015335 (DOI)000991872901067 ()2-s2.0-85147454505 (Scopus ID)978-1-6654-7661-4 (ISBN)978-1-6654-7662-1 (ISBN)
Conference
2022 Winter Simulation Conference, W.S.C. 2022 Guilin 11 December 2022 through 14 December 2022
Funder
Knowledge Foundation
Note

© 2022 IEEE

Intelligent Production Systems Division, University of Skövde

The authors gratefully acknowledge the Knowledge Foundation (KK-Stiftelsen), Sweden, for their upport and provision of research funding through the research profile Virtual Factories Knowledge-Driven Optimization (VF-KDO) at the University of Skövde, Sweden, in which this work is a part of it.

Available from: 2023-02-16 Created: 2023-02-16 Last updated: 2023-09-01Bibliographically approved
Smedberg, H., Barrera Diaz, C. A., Nourmohammadi, A., Bandaru, S. & Ng, A. H. C. (2022). Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems. Mathematical and Computational Applications, 27(6), Article ID 106.
Open this publication in new window or tab >>Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems
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2022 (English)In: Mathematical and Computational Applications, ISSN 1300-686X, E-ISSN 2297-8747, Vol. 27, no 6, article id 106Article in journal (Refereed) Published
Abstract [en]

Current market requirements force manufacturing companies to face production changes more often than ever before. Reconfigurable manufacturing systems (RMS) are considered a key enabler in today's manufacturing industry to cope with such dynamic and volatile markets. The literature confirms that the use of simulation-based multi-objective optimization offers a promising approach that leads to improvements in RMS. However, due to the dynamic behavior of real-world RMS, applying conventional optimization approaches can be very time-consuming, specifically when there is no general knowledge about the quality of solutions. Meanwhile, Pareto-optimal solutions may share some common design principles that can be discovered with data mining and machine learning methods and exploited by the optimization. In this study, the authors investigate a novel knowledge-driven optimization (KDO) approach to speed up the convergence in RMS applications. This approach generates generalized knowledge from previous scenarios, which is then applied to improve the efficiency of the optimization of new scenarios. This study applied the proposed approach to a multi-part flow line RMS that considers scalable capacities while addressing the tasks assignment to workstations and the buffer allocation problems. The results demonstrate how a KDO approach leads to convergence rate improvements in a real-world RMS case.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
multi-objective optimization, knowledge discovery, reconfigurable manufacturing system, simulation
National Category
Production Engineering, Human Work Science and Ergonomics Computer Sciences
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
urn:nbn:se:his:diva-22194 (URN)10.3390/mca27060106 (DOI)000904384800001 ()
Funder
Knowledge Foundation, 2018-0011
Note

CC BY 4.0

Correspondence: henrik.smedberg@his.se

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.

(This article belongs to the Special Issue Evolutionary Multi-objective Optimization: An Honorary Issue Dedicated to Professor Kalyanmoy Deb)

Available from: 2023-01-19 Created: 2023-01-19 Last updated: 2023-09-01Bibliographically approved
Projects
Virtual 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
ADOPTIVE – Automated Design & Optimisation of Vehicle Ergonomics [20200003]; University of Skövde; Publications
Perez Luque, E., Brolin, E., Högberg, D. & Lamb, M. (2022). Challenges for the Consideration of Ergonomics in Product Development in the Swedish Automotive Industry – An Interview Study. In: DESIGN2022: . Paper presented at DESIGN2022, 17th International Design Conference, May, 23-26, 2022, Croatia (pp. 2165-2174). Cambridge University Press, 2Hanson, L., Högberg, D., Brolin, E., Billing, E., Iriondo Pascual, A. & Lamb, M. (2022). Current Trends in Research and Application of Digital Human Modeling. In: Nancy L. Black; W. Patrick Neumann; Ian Noy (Ed.), Proceedings of the 21st Congress of the International Ergonomics Association (IEA 2021): Volume V: Methods & Approaches. Paper presented at 21st Congress of the International Ergonomics Association (IEA 2021), 13-18 June (pp. 358-366). Cham: SpringerMarshall, R., Brolin, E., Summerskill, S. & Högberg, D. (2022). Digital Human Modelling: Inclusive Design and the Ageing Population (1ed.). In: Sofia Scataglini; Silvia Imbesi; Gonçalo Marques (Ed.), Internet of Things for Human-Centered Design: Application to Elderly Healthcare (pp. 73-96). Singapore: Springer NatureKolbeinsson, A., Brolin, E. & Lindblom, J. (2021). Data-Driven Personas: Expanding DHM for a Holistic Approach. In: Julia L. Wright; Daniel Barber; Sofia Scataglini; Sudhakar L. Rajulu (Ed.), Advances in Simulation and Digital Human Modeling: Proceedings of the AHFE 2021 Virtual Conferences on Human Factors and Simulation, and Digital Human Modeling and Applied Optimization, July 25-29, 2021, USA. Paper presented at International Conference on Applied Human Factors and Ergonomics (AHFE 2021), USA, July 25-29, 2021. (pp. 296-303). Springer, 264Brolin, E., Högberg, D. & Hanson, L. (2020). Skewed Boundary Confidence Ellipses for Anthropometric Data. In: Lars Hanson, Dan Högberg, Erik Brolin (Ed.), DHM2020: Proceedings of the 6th International Digital Human Modeling Symposium, August 31 – September 2, 2020. Paper presented at 6th International Digital Human Modeling Symposium, August 31 – September 2, 2020, Skövde, Sweden (pp. 18-27). Amsterdam: IOS PressBrolin, E., Högberg, D. & Nurbo, P. (2020). Statistical Posture Prediction of Vehicle Occupants in Digital Human Modelling Tools. In: Vincent G. Duffy (Ed.), Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Posture, Motion and Health: 11th International Conference, DHM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part I. Paper presented at 11th International Conference, DHM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020 (pp. 3-17). Cham: Springer
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