Personalized manufacturing service composition recommendation: combining combinatorial optimization and collaborative fi
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Personalized manufacturing service composition recommendation: combining combinatorial optimization and collaborative filtering Shuangyao Zhao1,2 · Qiang Zhang1,2 · Zhanglin Peng1,2 · Xiaonong Lu1,2
© Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Manufacturing service composition · Service recommendation · Combinatorial optimization · Collaborative filtering
* Qiang Zhang [email protected] 1
School of Management, Hefei University of Technology, Hefei 230009, China
2
The MOE Key Laboratory of Process Optimization and Intelligent Decision‑Making, Ministry of Education, Hefei University of Technology, Hefei 230009, China
13
Vol.:(0123456789)
Journal of Combinatorial Optimization
1 Introduction Cloud manufacturing (CMfg) refers to a service-oriented networked manufacturing in which service users are enabled to configure, retrieve and invoke functionally similar resources, and compose the services to achieve the optimal condition (Bouzary and Frank Chen 2018; Zhou et al. 2018). Service composition, which various manufacturing resources and capabilities integrate into as virtualized services, has been recognized as a new value-added transformative model to meet the demands of customized manufacturing tasks (Wu et al. 2014; Li et al. 2018). As a large number of manufacturers begin to adopt the transformative model, the composition process can be transformed into “ho
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