IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v31y2020i3d10.1007_s10845-019-01472-1.html
   My bibliography  Save this article

SDF-GA: a service domain feature-oriented approach for manufacturing cloud service composition

Author

Listed:
  • Tianyang Li

    (Northeast Electric Power University, Jilin
    Harbin Institute of Technology)

  • Ting He

    (Huaqiao University)

  • Zhongjie Wang

    (Harbin Institute of Technology)

  • Yufeng Zhang

    (University of Birmingham)

Abstract

Cloud manufacturing (CMfg) is a new service-oriented manufacturing paradigm in which shared resources are integrated and encapsulated as manufacturing services. When a single service is not able to meet some manufacturing requirement, a composition of multiple services is then required via CMfg. Service composition and optimal selection (SCOS) is a key technique for creating an on-demand quality of service (QoS)-optimal efficient manufacturing service composition to satisfy various user requirements. Given the number of services with the same functionality and a similar level of QoS, SCOS has been seen as a key challenge in CMfg research. One effective approach to solving SCOS problems is to use service domain features (SDF) through investigating the probability of services being used for a specific requirement from multiple perspectives. The approach can result in a division of the service space and then help streamline the service space with large-scale candidate services. The approach can also search for optimal subspaces that most likely contribute to an overall optimal solution. Accordingly, this paper develops an SDF-oriented genetic algorithm to effectively create a manufacturing service composition with large-scale candidate services. Fine-grained SDF definitions are developed to divide the service space. SDF-based optimization strategies are adopted. The novelty of the proposed algorithm is presented based on Bayes’ theorem. The effectiveness of the proposed algorithm is validated by solving three real-world SCOS problems in a private CMfg.

Suggested Citation

  • Tianyang Li & Ting He & Zhongjie Wang & Yufeng Zhang, 2020. "SDF-GA: a service domain feature-oriented approach for manufacturing cloud service composition," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 681-702, March.
  • Handle: RePEc:spr:joinma:v:31:y:2020:i:3:d:10.1007_s10845-019-01472-1
    DOI: 10.1007/s10845-019-01472-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-019-01472-1
    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/s10845-019-01472-1?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. Hong Jin & Xifan Yao & Yong Chen, 2017. "Correlation-aware QoS modeling and manufacturing cloud service composition," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1947-1960, December.
    2. Hua Guo & Lin Zhang & Yilong Liu & Fei Tao & Min Shu & Shaomin Mu, 2014. "A discovery method of service-correlation for service composition in virtual enterprise," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 8(5), pages 579-618.
    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. F. Tao & Y. Cheng & L. Zhang & A. Y. C. Nee, 2017. "Advanced manufacturing systems: socialization characteristics and trends," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1079-1094, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Reza Vatankhah Barenji, 2022. "A blockchain technology based trust system for cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1451-1465, June.
    2. Hongbin Wang & Yang Ding & Hanchuan Xu, 2024. "Particle swarm optimization service composition algorithm based on prior knowledge," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 35-53, January.

    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. Hao Li & Shanghua Mi & Qifeng Li & Xiaoyu Wen & Dongping Qiao & Guofu Luo, 2020. "A scheduling optimization method for maintenance, repair and operations service resources of complex products," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1673-1691, October.
    2. Yu Feng & Biqing Huang, 2020. "Cloud manufacturing service QoS prediction based on neighbourhood enhanced matrix factorization," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1649-1660, October.
    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. Uwizeyemungu, Sylvestre & Poba-Nzaou, Placide & St-Pierre, Josée, 2022. "Back-end information technology resources and manufacturing SMEs’ export commitment: An empirical investigation," International Business Review, Elsevier, vol. 31(5).
    5. Vendrell-Herrero, Ferran & Bustinza, Oscar F. & Opazo-Basaez, Marco, 2021. "Information technologies and product-service innovation: The moderating role of service R&D team structure," Journal of Business Research, Elsevier, vol. 128(C), pages 673-687.
    6. Gedas Baranauskas & Agota Giedrė Raišienė & Renata Korsakienė, 2020. "Mapping the Scientific Research on Mass Customization Domain: A Critical Review and Bibliometric Analysis," JRFM, MDPI, vol. 13(9), pages 1-20, September.
    7. Xuhui Xia & Wei Liu & Zelin Zhang & Lei Wang & Jianhua Cao & Xiang Liu, 2019. "A Balancing Method of Mixed-model Disassembly Line in Random Working Environment," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
    8. Bustinza, Oscar F. & Opazo-Basaez, Marco & Tarba, Shlomo, 2022. "Exploring the interplay between Smart Manufacturing and KIBS firms in configuring product-service innovation performance," Technovation, Elsevier, vol. 118(C).
    9. Daozhi Zhao & Yang Xue & Cejun Cao & Hongshuai Han, 2019. "Channel Selection and Pricing Decisions Considering Three Charging Modes of Production Capacity Sharing Platform: A Sustainable Operations Perspective," Sustainability, MDPI, vol. 11(21), pages 1-28, October.
    10. Quan Xiao & Shanshan Wan & Fucai Lu & Shun Li, 2019. "Risk Assessment for Engagement in Sharing Economy of Manufacturing Enterprises: A Matter–Element Extension Based Approach," Sustainability, MDPI, vol. 11(17), pages 1-29, September.
    11. Federica Costa & Alberto Portioli-Staudacher, 2021. "Labor flexibility integration in workload control in Industry 4.0 era," Operations Management Research, Springer, vol. 14(3), pages 420-433, December.
    12. Li, Lei & Huang, Haihong & Zou, Xiang & Zhao, Fu & Li, Guishan & Liu, Zhifeng, 2021. "An energy-efficient service-oriented energy supplying system and control for multi-machine in the production line," Applied Energy, Elsevier, vol. 286(C).
    13. 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.
    14. Shi‐Xiao Wang & Wen‐Min Lu & Shiu‐Wan Hung, 2020. "Improving innovation efficiency of emerging economies: The role of manufacturing," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(4), pages 503-519, June.
    15. Yimeng Jin & Fei Hu & Jin Qi, 2022. "Multidimensional Characteristics and Construction of Classification Model of Prosumers," Sustainability, MDPI, vol. 14(19), pages 1-21, September.
    16. 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.
    17. Beatriz Ferreira & Carla Curado & Mírian Oliveira, 2022. "The Contribution of Knowledge Management to Human Resource Development: a Systematic and Integrative Literature Review," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 13(3), pages 2319-2347, September.
    18. Bonomi, Sabrina & Sarti, Daria & Torre, Teresina, 2020. "Creating a collaborative network for welfare services in public sector. A knowledge-based perspective," Journal of Business Research, Elsevier, vol. 112(C), pages 440-449.
    19. Lucianetti, Lorenzo & Chiappetta Jabbour, Charbel Jose & Gunasekaran, Angappa & Latan, Hengky, 2018. "Contingency factors and complementary effects of adopting advanced manufacturing tools and managerial practices: Effects on organizational measurement systems and firms' performance," International Journal of Production Economics, Elsevier, vol. 200(C), pages 318-328.
    20. Pingyu Jiang & Pulin Li, 2019. "Shared factory: a new production node for social manufacturing in the context of sharing economy," Papers 1904.11377, arXiv.org.

    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:joinma:v:31:y:2020:i:3:d:10.1007_s10845-019-01472-1. 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.