Skip to main content
SpringerLink
Log in

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

  • Published:
Journal of Combinatorial Optimization Aims and scope Submit manuscript

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.

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

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Ardagna D, Pernici B (2005) Global and local QoS guarantee in web service selection. In: Proceedings of business process management workshops. pp 32–46

  • Berbner R, Spahn M, Repp N, Heckmann O, Steinmetz R (2006) Heuristics for QoS-aware web service composition. In: IEEE international conference on web services

  • Bo-Hu LI, Zhang L, Wang SL, Tao F, Cao JW, Jiang XD, Song X, Chai XD (2010) Cloud manufacturing: a new service-oriented networked manufacturing model. Comput Integr Manuf Syst 16:1–7

    Google Scholar 

  • Bouzary H, Frank Chen F (2018) Service optimal selection and composition in cloud manufacturing: a comprehensive survey. Int J Adv Manuf Technol 97:795–808

    Article  Google Scholar 

  • Fei T, Hu Y, Zhou Z (2010) Correlation-aware resource service composition and optimal-selection in manufacturing grid. Eur J Oper Res 201:129–143

    Article  Google Scholar 

  • Fujii K, Suda T (2005) Semantics-based dynamic service composition. IEEE J Sel Areas Commun 23:2361–2372

    Article  Google Scholar 

  • Gabrel V (2012) A new 0–1 linear program for QoS and transactional-aware web service composition. In: IEEE symposium on computers and communications

  • Gao N, Zhao S, Zhang X (2009) Research on the service-oriented manufacturing model. In: IEEE international conference on industrial engineering and engineering management

  • Gohar P, Purohit L (2016) Discovery and prioritization of web services based on fuzzy user preferences for QoS. In: International conference on computer

  • Guo H, Tao F, Zhang L, Laili YJ, Liu DK (2012) Research on measurement method of resource service composition flexibility in service-oriented manufacturing system. Int J Comput Integr Manuf 25:113–135

    Article  Google Scholar 

  • Guobing Z, Ming J, Sen N, Hao W, Shengye P, Yanglan G (2018) QoS-aware web service recommendation with reinforced collaborative filtering: service-oriented computing. In: 16th international conference, ICSOC 2018. Proceedings: lecture notes in computer science (LNCS 11236), p 430–445

  • GutierrezGarcia JO, Mong K (2013) Agent-based cloud service composition. Appl Intell 38:436–464

    Article  Google Scholar 

  • Huang B, Li C, Fei T (2013) A chaos control optimal algorithm for QoS-based service composition selection in cloud manufacturing system. Enterprise Inf Syst 8:445–463

    Article  Google Scholar 

  • Leitão P, Mendes JM, Bepperling A, Cachapa D, Colombo AW, Restivo F (2012) Integration of virtual and real environments for engineering service-oriented manufacturing systems. J Intell Manuf 23:2551–2563

    Article  Google Scholar 

  • Li W, He YX (2011) A web service composition algorithm based on global QoS optimizing with MOCACO. In: Algorithms & architectures for parallel processing, International conference, Ica3pp, Busan, Korea, May. DBLP

  • Li C, Guan J, Liu T, Ma N, Zhang J (2018) An autonomy-oriented method for service composition and optimal selection in cloud manufacturing. Int J Adv Manuf Technol 96:1–22

    Article  Google Scholar 

  • Lin P, Zhang XB (2013) The inverse optimal allocation model of manufacturing resource for small and medium-sized manufacturing enterprises in grid environment. Appl Mech Mater 273:22–27

    Article  Google Scholar 

  • Liu N, Li X, Shen W (2014) Multi-granularity resource virtualization and sharing strategies in cloud manufacturing. J Netw Comput Appl 46:72–82

    Article  Google Scholar 

  • Menascé DA, Casalicchio E, Dubey V (2010) On optimal service selection in service oriented architectures. Perform Eval 67:659–675

    Article  Google Scholar 

  • Milanovic N, Malek M (2004) Current solutions for Web service composition. IEEE Internet Comput 8:51–59

    Article  Google Scholar 

  • Morariu O, Morariu C, Borangiu T (2016) Shop-floor resource virtualization layer with private cloud support. J Intell Manuf 27:447–462

    Article  Google Scholar 

  • Namjoo MR, Keramati A (2018) Analysing causal dependencies of composite service resilience in cloud manufacturing using resource-based theory and DEMATEL method. Int J Comput Integr Manuf 31:942–960

    Article  Google Scholar 

  • Rodgers JL, Nicewander WA (1988) Thirteen ways to look at the correlation coefficient. Am Stat 42:59–66

    Article  Google Scholar 

  • Saaty TL (2003) Decision-making with the AHP: why is the principal eigenvector necessary. Eur J Oper Res 145:85–91

    Article  MathSciNet  Google Scholar 

  • Tao F, Zhao D, Zhang L (2010) Resource service optimal-selection based on intuitionistic fuzzy set and non-functionality QoS in manufacturing grid system. Knowl Inf Syst 25:185–208

    Article  Google Scholar 

  • Wang X, Wong TN, Wang G (2012) Service-oriented architecture for ontologies supporting multi-agent system negotiations in virtual enterprise. J Intell Manuf 23:1331–1349

    Article  Google Scholar 

  • Wu Q, Zhu Q, Zhou M (2014) A correlation-driven optimal service selection approach for virtual enterprise establishment. J Intell Manuf 25:1441–1453

    Article  Google Scholar 

  • Xue X, Wang S, Lu B (2016) Manufacturing service composition method based on networked collaboration mode. J Netw Comput Appl 59:28–38

    Article  Google Scholar 

  • Xue X, Wang S, Zhang L, Qin C (2018) Complexity analysis of manufacturing service ecosystem: a mapping-based computational experiment approach. Int J Prod Res 57:1–22

    Article  Google Scholar 

  • Yang YS, Lei WJL, Tao H (2008) Service-correlation aware service selection for composite service. Chin J Comput 31:1383–1397

    Google Scholar 

  • Zeng L, Benatallah B, Ngu AHH, Dumas M, Kalagnanam J, Chang H (2004) QoS-aware middleware for Web services composition. IEEE Trans Softw Eng 3:449–470

    Google Scholar 

  • Zhang WY, Zhang S, Chen YG, Pan XW (2013) Combining social network and collaborative filtering for personalised manufacturing service recommendation. Int J Prod Res 51:6702–6719

    Article  Google Scholar 

  • Zheng Z, Ma H, Lyu MR, King I (2011) QoS-aware web service recommendation by collaborative filtering. IEEE Trans Serv Comput 4:140–152

    Article  Google Scholar 

  • Zheng H, Feng Y, Tan J (2016) A fuzzy QoS-aware resource service selection considering design preference in cloud manufacturing system. Int J Adv Manuf Technol 84:371–379

    Article  Google Scholar 

  • Zhong Y, Fan YS, Tan W, Zhang J (2018) Web service recommendation with reconstructed profile from mashup descriptions. IEEE Trans Autom Sci Eng 15:468–478

    Article  Google Scholar 

  • Zhou JJ, Yao XF (2017) A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition. Int J Prod Res 55:4765–4784

    Article  Google Scholar 

  • Zhou J, Yao X, Lin Y, Chan FTS, Li Y (2018) An adaptive multi-population differential artificial bee colony algorithm for many-objective service composition in cloud manufacturing. Inf Sci 456:50–82

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This research is supported by grants from the National Natural Science Foundation of China [No. NSFC 71690230/G0103], [No. NSFC 71690235/G0110], [No. NSFC 71501055], [No. NSFC 71601066].

Author information

Authors and Affiliations

  1. School of Management, Hefei University of Technology, Hefei, 230009, China

    Shuangyao Zhao, Qiang Zhang, Zhanglin Peng & Xiaonong Lu

  2. The MOE Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei University of Technology, Hefei, 230009, China

    Shuangyao Zhao, Qiang Zhang, Zhanglin Peng & Xiaonong Lu

Authors
  1. Shuangyao Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhanglin Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaonong Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qiang Zhang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, S., Zhang, Q., Peng, Z. et al. Personalized manufacturing service composition recommendation: combining combinatorial optimization and collaborative filtering. J Comb Optim 40, 733–756 (2020). https://doi.org/10.1007/s10878-020-00613-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10878-020-00613-0

Keywords

Search

Navigation