Skip to main content

Advertisement

SpringerLink
Log in

The case-library method for service composition and optimal selection of big manufacturing data in cloud manufacturing system

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Service composition and optimal selection (SCOS) technology composes basic services to satisfy various users’ needs and realize enterprise resource efficient allocation and value maximization. Since sharply increasing scale of the cloud manufacturing (CMfg) resource pool, and the growing sophistication of user requests, will make composed service a sharp increase in the quantity, type, dimension and complexity, which cause a big data environment for CMfg’s application and realization, this paper analyzes the difficulties and solutions of SCOS of big data in the future, especially for optimal selection from large-scale composed service execute paths (CSEP), and proposes two phases SCOS method based on case library. Firstly, cases, similar with users’ request, are searched from case library. Secondly, the cases are used to initial the existing optimization algorithm to solve large-scale optimal selection problem. Moreover, case library structure, user’s service request structure, similarity comparison, and realization process are studied. Compared to the existing optimization algorithm method, the prototype system using case library in large-scale CMfg could obtain a better optimization result.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Li BH, Zhang L, Wang SL, Tao F (2010) Cloud manufacturing: a new service-oriented manufacturing model. Comput Integr Manuf Syst 16(1):1–8

    Google Scholar 

  2. Tao F, Zhao D, Zhang L Resource service optimal-selection based on intuitionistic fuzzy set and non-functionality QoS in manufacturing grid system. Knowl Inf Syst 25(1):185–208

  3. Tao F, Cheng Y, Zhang L, Nee AYC (2015) Advanced manufacturing systems: socialization characteristics and trends. J Int Manuf. doi:10.1007/s10845-015-1042-8

    Google Scholar 

  4. Tao F, Li C, Liao TW, Laili YJ. BGM-BLA: a new algorithm for dynamic migration of virtual machines in cloud computing IEEE Trans Serv Comput doi:10.1109/TSC.2015.2416928

  5. Tao F, Laili YJ, Xu L, Zhang L (2013) FC-PACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Trans Ind Inf 9(4):2023–2033

    Article  Google Scholar 

  6. Tao F, Cheng Y, Xu L, Zhang L, Li B (2014) CCIoT-CMfg: cloud computing and internet of things based cloud manufacturing service system. IEEE Trans Ind Inf 10(2):1435–1442

    Article  Google Scholar 

  7. Tao F, Zhang L, Liu YK, Cheng Y, Wang LH, Xun X, (2015) Manufacturing service management in cloud manufacturing: overview and future research directions. J Manuf Sci Eng-Trans ASME (Accepted on March 18, 2015) doi:10.1115/1.4030510

  8. European Commission, 2014, available from http://cordis.europa.eu/fp7/ict/computing/home-i4ms_en.html

  9. (2013) Enhancing the product realization process with cloud-based design and manufacturing systems. Trans ASME J Comput Inf Sci Eng 13(4)

  10. Jazdi N (2014) Cyber physical systems in the context of industry 4.0. IEEE 19th International Conference on Automation, Quality and Testing, Robotics (THETA), May 22–24

  11. Gerald R, Frank B, Juergen G. (2013) Intelligent manufacturing operations planning, scheduling and dispatching on the basis of virtual machine tools. IFIP TC 5 International Conference on Project Research On Leading-edge Applications and Methods for Applied Information Technology, Oct. 10–11, 2013

  12. Tao F, Hu YF, Zhou ZD (2008) Study on manufacturing grid & its resource service optimal-selection system. Int J Adv Manuf Technol 37(9–10):1022–1041

    Article  Google Scholar 

  13. Xiang F, Hu YF, Yu YR, Wu HC (2014) Service composition and its optimal-selection based on QoS and energy consumption in cloud manufacturing. CEJOR 22(4):663–685

    Article  Google Scholar 

  14. Tao F, Zuo Y et al (2014) IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing [J]. IEEE Trans Ind Inf 10(2):1547–1557

    Article  Google Scholar 

  15. Xiang F, Hu YF. (2012) Cloud manufacturing resource access system based on Internet of Things. 2nd International Conference on Frontiers of Manufacturing and Design Science (ICFMD 2011), Dec. 11–13. Taiwan, pp. 2421–2425

  16. Tao F, Guo H, Zhang L, Cheng Y (2012) Modeling of combinable relationship-based composition service network and theoretical proof of its scale-free characteristics. Enterp Inf Syst 6(4):373–404

    Article  Google Scholar 

  17. Liu C, Ranjan R, Yang C, Zhang XY et al (2015) MuR-DPA: top-down levelled multi-replica Merkle Hash tree based secure public auditing for dynamic big data storage on cloud. IEEE Trans Comput 64(9):2609–2622

    Article  MathSciNet  Google Scholar 

  18. Shaw MJ, Subramaniam C, Tan GW, Welge ME (2001) Knowledge management and data mining for marketing. Decis Support Syst 31(1):127–137

    Article  Google Scholar 

  19. Li JR, Tao F, Cheng Y, Zhao LJ (2015) Big Data in product data management. Int J Adv Manuf Technol. doi:10.1007/s00170-015-7151-x

    Google Scholar 

  20. Yan HZ, Lei F, Zhi Y (2011) Optimization of cloud database route scheduling based on combination of genetic algorithm and ant colony algorithm. Procedia Eng 15:3341–3345

    Article  Google Scholar 

  21. Bennett DP, Yano CA (2004) A decomposition approach for an equipment selection and multiple product routing problem incorporation environmental factors. Eur J Oper Res 156(3):643–664

    Article  MATH  Google Scholar 

  22. Tao F et al (2008) Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system. IEEE Trans Ind Inf 4(4):315–327

    Article  Google Scholar 

  23. Rajesh R, Pugazhendhi S, Ganesh K (2012) Simulated annealing algorithm for balanced allocation problem. Int J Comput Integr Manuf 61(5–8):431–440

    Google Scholar 

  24. Chen AL, Yang GK, Wu ZM (2008) Production scheduling optimization algorithm for the hot rolling processes. Int J Prod Res 46(7):1955–1973

    Article  MATH  Google Scholar 

  25. Pitts RA, Ventura JA (2009) Scheduling flexible manufacturing cells using tabu search. Int J Prod Res 47(24):6907–6928

    Article  MATH  Google Scholar 

  26. Tang KS et al (2011) A theoretical development and analysis of jumping gene genetic algorithm. IEEE Trans Ind Inf 7(3):408–418

    Article  Google Scholar 

  27. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization-artificial ants as a computational intelligence technique. IEEE Comput Int Mag 1(4):28–39

    Article  Google Scholar 

  28. Udhayakumar P, Kumanan S (2012) Integrated scheduling of flexible manufacturing system using evolutionary algorithms. Proc IMechE B J Eng Manuf 61(5–8):621–635

    Google Scholar 

  29. Tao F, Zhang L, Zhang ZH, Nee AYC (2010) A quantum multi-agent evolutionary algorithm for selection of partners in a virtual enterprise. CIRP Ann Manuf Technol 59(1):485–488

    Article  Google Scholar 

  30. Guo H, Tao F et al (2010) Correlation-aware resource service composition and optimal-selection in manufacturing grid. Eur J Oper Res 201(1):129–143

    Article  MATH  Google Scholar 

  31. Jin H, Yao XF, Chen Y (2015) Correlation-aware QoS modeling and manufacturing cloud service composition. J Int Manuf

  32. Leonardo A, Gustavo A, Guilherme M et al (2013) A systematic literature review of service choreography adaptation. SOCA 7:199–216

    Article  Google Scholar 

  33. Jan M, Michael H (2008) From WS-CDL choreography to BPEL process orchestration. J Enterp lnf Manag 21(5):525–542

    Google Scholar 

  34. Casati F, llnicki S, Jin L, Krishnamoorthy V, Shan MC. (2000) Adaptive and dynamic service composition in eflow. Proceedings of the 12th Int. Conf. on Advanced Information Systems Engineering. Springer, Berlin, pp. 13–31

  35. Moore JW (2006) Converging software and systems engineering standards [J]. Computer 39(9):106–108

    Article  Google Scholar 

  36. Tao F, Zhang L, Venkatesh VC, Luo YL, Cheng Y (2011) Cloud manufacturing: a computing and service-oriented manufacturing model [J]. J Eng Manuf (Proc IMechE B J Eng Manuf) 225(10):1969–1976

    Article  Google Scholar 

  37. Tao F, Laili YJ, Liu YL, Feng Y, Wang Q, Zhang L, Xu L (2014) Concept, principle and application of configurable intelligent optimization algorithm. IEEE Syst J 8(1):28–42

    Article  Google Scholar 

  38. Tao F, Feng Y, Zhang L, Liao TW (2014) CLPS-GA: a case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling [J]. Appl Soft Comput 19:264–279

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan, 430081, China

    Feng Xiang, GuoZhang Jiang, LuLu Xu & NianXian Wang

Authors
  1. Feng Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. GuoZhang Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. LuLu Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. NianXian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feng Xiang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiang, F., Jiang, G., Xu, L. et al. The case-library method for service composition and optimal selection of big manufacturing data in cloud manufacturing system. Int J Adv Manuf Technol 84, 59–70 (2016). https://doi.org/10.1007/s00170-015-7813-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-015-7813-8

Keywords

Search

Navigation