IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/vyid10.1007_s10878-020-00613-0.html
   My bibliography  Save this article

Personalized manufacturing service composition recommendation: combining combinatorial optimization and collaborative filtering

Author

Listed:
  • Shuangyao Zhao

    (Hefei University of Technology
    Hefei University of Technology)

  • Qiang Zhang

    (Hefei University of Technology
    Hefei University of Technology)

  • Zhanglin Peng

    (Hefei University of Technology
    Hefei University of Technology)

  • Xiaonong Lu

    (Hefei University of Technology
    Hefei University of Technology)

Abstract

Owing to the rapid proliferation of service technologies in cross-enterprise manufacturing collaborations, manufacturing service composition (MSC) has attracted much attention from both academia and industries. However, the existing service composition is often constructed by the combination of off-line and on-line services, quality of service (QoS) attributes are not appropriate for satisfying the specific demands of MSC. Moreover, there are very few historical QoS invocations of manufacturing service, leading to difficulty in recommending appropriate service composition to a target user. In order to find the personalized MSC mode from a complex service network more accurately, we combine combinatorial optimization with collaborative filtering in this paper to figure out two questions: (1) how to construct a QoS description model of manufacturing service composition; (2) how to enhance the effectiveness of personalized QoS-aware service composition recommendations. First, the new QoS model of MSC is proposed by considering both traditional characteristics (e.g. availability, performance and reliability), variability of service composition and enterprise dimensional QoS attributes. Second, the service combination optimization is constructed based on combinatorial optimization method. Third, the collaborative filtering is employed to calculate the missing QoS values of the candidate manufacturing services. Finally, with both available objective functions and predicted QoS values, optimal service composition recommendation can be generated by using combinatorial optimization model with QoS constraints.

Suggested Citation

  • Shuangyao Zhao & Qiang Zhang & Zhanglin Peng & Xiaonong Lu, 0. "Personalized manufacturing service composition recommendation: combining combinatorial optimization and collaborative filtering," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-24.
    • Handle: RePEc:spr:jcomop:v::y::i::d:10.1007_s10878-020-00613-0
    DOI: 10.1007/s10878-020-00613-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-020-00613-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10878-020-00613-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jiajun Zhou & Xifan Yao, 2017. "A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition," International Journal of Production Research, Taylor & Francis Journals, vol. 55(16), pages 4765-4784, August.
    2. Saaty, Thomas L., 2003. "Decision-making with the AHP: Why is the principal eigenvector necessary," European Journal of Operational Research, Elsevier, vol. 145(1), pages 85-91, February.
    3. Tao, Fei & Zhao, Dongming & Yefa, Hu & Zhou, Zude, 2010. "Correlation-aware resource service composition and optimal-selection in manufacturing grid," European Journal of Operational Research, Elsevier, vol. 201(1), pages 129-143, February.
    4. Octavian Morariu & Cristina Morariu & Theodor Borangiu, 2016. "Shop-floor resource virtualization layer with private cloud support," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 447-462, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shuangyao Zhao & Qiang Zhang & Zhanglin Peng & Xiaonong Lu, 2020. "Personalized manufacturing service composition recommendation: combining combinatorial optimization and collaborative filtering," Journal of Combinatorial Optimization, Springer, vol. 40(3), pages 733-756, October.
    2. Wei He & Guozhu Jia & Hengshan Zong & Tao Huang, 2019. "Multi-Objective Cloud Manufacturing Service Selection and Scheduling with Different Objective Priorities," Sustainability, MDPI, vol. 11(17), pages 1-24, September.
    3. Yankai Wang & Shilong Wang & Bo Yang & Bo Gao & Sibao Wang, 2022. "An effective adaptive adjustment method for service composition exception handling in cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 735-751, March.
    4. Fang, Lei, 2022. "Measuring and decomposing group performance under centralized management," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1006-1013.
    5. Seyed Rakhshan & Ali Kamyad & Sohrab Effati, 2015. "Ranking decision-making units by using combination of analytical hierarchical process method and Tchebycheff model in data envelopment analysis," Annals of Operations Research, Springer, vol. 226(1), pages 505-525, March.
    6. Carmen Herrero & Antonio Villar, 2022. "Sports competitions and the Break-Even rule," Working Papers 22.13, Universidad Pablo de Olavide, Department of Economics.
    7. Madjid Tavana & Mariya Sodenkamp & Leena Suhl, 2010. "A soft multi-criteria decision analysis model with application to the European Union enlargement," Annals of Operations Research, Springer, vol. 181(1), pages 393-421, December.
    8. Abdelmonaim Okacha & Adil Salhi & Kamal Abdelrahman & Hamid Fattasse & Kamal Lahrichi & Kaoutar Bakhouya & Biraj Kanti Mondal, 2024. "Balancing Environmental and Human Needs: Geographic Information System-Based Analytical Hierarchy Process Land Suitability Planning for Emerging Urban Areas in Bni Bouayach Amid Urban Transformation," Sustainability, MDPI, vol. 16(15), pages 1-24, July.
    9. Zola, Fernanda Cavicchioli & Colmenero, João Carlos & Aragão, Franciely Velozo & Rodrigues, Thaisa & Junior, Aldo Braghini, 2020. "Multicriterial model for selecting a charcoal kiln," Energy, Elsevier, vol. 190(C).
    10. Baghersad, Milad & Zobel, Christopher W., 2015. "Economic impact of production bottlenecks caused by disasters impacting interdependent industry sectors," International Journal of Production Economics, Elsevier, vol. 168(C), pages 71-80.
    11. Aniruddh Nain & Deepika Jain & Shivam Gupta & Ashwani Kumar, 2023. "Improving First Responders' Effectiveness in Post-Disaster Scenarios Through a Hybrid Framework for Damage Assessment and Prioritization," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(3), pages 409-437, September.
    12. Andrzej Pacana & Dominika Siwiec & Robert Ulewicz & Malgorzata Ulewicz, 2024. "A Novelty Model Employing the Quality Life Cycle Assessment (QLCA) Indicator and Frameworks for Selecting Qualitative and Environmental Aspects for Sustainable Product Development," Sustainability, MDPI, vol. 16(17), pages 1-24, September.
    13. Roh, Seungkook & Choi, Jae Young & Chang, Soon Heung, 2019. "Modeling of nuclear power plant export competitiveness and its implications: The case of Korea," Energy, Elsevier, vol. 166(C), pages 157-169.
    14. Wu, Zhibin & Huang, Shuai & Xu, Jiuping, 2019. "Multi-stage optimization models for individual consistency and group consensus with preference relations," European Journal of Operational Research, Elsevier, vol. 275(1), pages 182-194.
    15. Valdecy Pereira & Helder Costa, 2015. "Nonlinear programming applied to the reduction of inconsistency in the AHP method," Annals of Operations Research, Springer, vol. 229(1), pages 635-655, June.
    16. Carlos Andrés Vergara Tamayo & Diana Carolina Ortiz Motta, 2016. "Contribución al desarrollo sostenible local de los proyectos MDL en el sector de generación eléctrica por biomasa: caso INCAUCA S.A," Revista Facultad de Ciencias Económicas, Universidad Militar Nueva Granada, vol. 24(2), pages 161-182, October.
    17. Herbert Dawid & Reinhold Decker & Thomas Hermann & Hermann Jahnke & Wilhelm Klat & Rolf König & Christian Stummer, 2017. "Management science in the era of smart consumer products: challenges and research perspectives," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(1), pages 203-230, March.
    18. Gerda Ana Melnik-Leroy & Gintautas Dzemyda, 2021. "How to Influence the Results of MCDM?—Evidence of the Impact of Cognitive Biases," Mathematics, MDPI, vol. 9(2), pages 1-25, January.
    19. Javeria Saleem & Sheikh Saeed Ahmad & Amna Butt, 2020. "Hazard risk assessment of landslide-prone sub-Himalayan region by employing geospatial modeling approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(3), pages 1497-1514, July.
    20. B. Domenech & L. Ferrer-Martí & R. Pastor, 2022. "Multicriteria analysis of renewable-based electrification projects in developing countries," Annals of Operations Research, Springer, vol. 312(2), pages 1375-1401, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcomop:v::y::i::d:10.1007_s10878-020-00613-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.