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.
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Acknowledgements
This work has been supported in part by the research projects the National Natural Science Foundation of China (NSFC) (No. 71571056) and the Scientific Research Funds of Huaqiao University (16BS304).
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Appendix
Appendix
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1.
Proof of Lemma2. A set of values of \( P_{T - GA} \left( {B/A_{h}^{\left( 0 \right)} } \right) \) can be derived based on the formula in Property 1 for calculating \( P_{T - GA} \left( {B/A_{h}^{\left( 0 \right)} } \right) \) and sorted in ascending order \( 0 \le P_{T - GA} \left( {B/A_{1}^{\left( 0 \right)} } \right) \le P_{T - GA} \left( {B/A_{2}^{\left( 0 \right)} } \right) \le \cdots \le P_{T - GA} \left( {B/A_{\text{h}}^{\left( 0 \right)} } \right) \), and then we randomly split the set of values as \( 0 \le P_{T - GA} \left( {B/A_{1}^{\left( 0 \right)} } \right) \le P_{T - GA} \left( {B/A_{2}^{\left( 0 \right)} } \right) \le \cdots \le P_{T - GA} \left( {B/A_{h - u}^{\left( 0 \right)} } \right) \le P_{T - GA} \left( {B/A_{1}^{\left( 0 \right)} } \right) \le P_{T - GA} \left( {B/A_{2}^{\left( 0 \right)} } \right) \le \cdots \le P_{T - GA} \left( {B/A_{u}^{\left( 0 \right)} } \right) \). We assure that the optimal subspace is one of subspaces among u subspaces with bigger values of \( P_{T - GA} \left( {B/A_{u}^{\left( 0 \right)} } \right) \) and use all solutions from u subspaces to initialize the population of SDFs-GA. Based on the Property 1, we can have
$$ \begin{aligned} P_{SDF - GA} \left( {A^{\left( 0 \right)} } \right) = & \mathop \sum \limits_{j = 1}^{u} P_{SDF - GA} \left( {A_{j}^{\left( 0 \right)} } \right) = \frac{{\mathop \sum \nolimits_{j = 1}^{u} P_{T - GA} \left( {B/A_{j}^{\left( 0 \right)} } \right) \times P_{T - GA} \left( {A_{j}^{\left( 0 \right)} } \right)}}{{\mathop \sum \nolimits_{i = 1}^{h - u} P_{T - GA} \left( {B/A_{i}^{\left( 0 \right)} } \right) \times P_{T - GA} \left( {A_{i}^{\left( 0 \right)} } \right) + \mathop \sum \nolimits_{j = 1}^{u} P_{T - GA} \left( {B/A_{j}^{\left( 0 \right)} } \right) \times P_{T - GA} \left( {A_{j}^{\left( 0 \right)} } \right)}} \\ = & \frac{{\mathop \sum \nolimits_{j = 1}^{u} \frac{{P_{T - GA} \left( {B/A_{j}^{\left( 0 \right)} } \right)}}{{\hbox{min} P_{T - GA} \left( {B/A_{j}^{\left( 0 \right)} } \right)}} \times P_{T - GA} \left( {A_{j}^{\left( 0 \right)} } \right)}}{{\mathop \sum \nolimits_{i = 1}^{h - u} \frac{{P_{T - GA} \left( {B/A_{i}^{\left( 0 \right)} } \right)}}{{\hbox{min} P_{T - GA} \left( {B/A_{j}^{\left( 0 \right)} } \right)}} \times P_{T - GA} \left( {A_{i}^{\left( 0 \right)} } \right) + \mathop \sum \nolimits_{j = 1}^{u} \frac{{P_{T - GA} \left( {B/A_{j}^{\left( 0 \right)} } \right)}}{{\hbox{min} P_{T - GA} \left( {B/A_{j}^{\left( 0 \right)} } \right)}} \times P_{T - GA} \left( {A_{j}^{\left( 0 \right)} } \right)}} \\ \end{aligned} $$
Because of that the value of \( P_{T - GA} \left( {B/A_{i}^{\left( 0 \right)} } \right) \) is smaller than the value of \( \hbox{min} P_{T - GA} \left( {B/A_{j}^{\left( 0 \right)} } \right) \), so we can have
On account of \( \sum\nolimits_{i = 1}^{h - u} {P_{T - GA} } \left( {A_{i}^{\left( t \right)} } \right) + \sum\nolimits_{j = 1}^{u} {P_{T - GA} } \left( {A_{j}^{\left( t \right)} } \right) = 1 \), we can have
Because of \( \sum\nolimits_{j = 1}^{u} {\left( {\frac{{P_{T - GA} \left( {B/A_{j}^{\left( 0 \right)} } \right)}}{{\hbox{min} P_{T - GA} \left( {B/A_{j}^{\left( 0 \right)} } \right)}} - 1} \right)} \times P_{T - GA} \left( {A_{j}^{\left( 0 \right)} } \right) \ge 0 \), so we can have
- 2.
Proof of Lemma3. Based on the Property 1, we can have
$$ \begin{aligned} P_{T - GA} \left( {A^{\left( 1 \right)} } \right) = & \frac{{\mathop \sum \nolimits_{j = 1}^{u} P\left( {B/A_{j}^{\left( 0 \right)} } \right)P\left( {A_{j}^{\left( 0 \right)} } \right)}}{{\mathop \sum \nolimits_{i = 1}^{h - u} P\left( {B/A_{i}^{\left( 0 \right)} } \right)P\left( {A_{i}^{\left( 0 \right)} } \right) + \mathop \sum \nolimits_{j = 1}^{u} P\left( {B/A_{j}^{\left( 0 \right)} } \right)P\left( {A_{j}^{\left( 0 \right)} } \right)}} \\ = & \frac{1}{{\frac{{\mathop \sum \nolimits_{i = 1}^{h - u} P\left( {B/A_{i}^{\left( 0 \right)} } \right)P\left( {A_{i}^{\left( 0 \right)} } \right)}}{{\mathop \sum \nolimits_{j = 1}^{u} P\left( {B/A_{j}^{\left( 0 \right)} } \right)P\left( {A_{j}^{\left( 0 \right)} } \right)}} + 1}} \\ \end{aligned} $$
Because that the operators of SDFs-GA use feature sets generated from \( A_{GA}^{\left( 1 \right)} \), we can have
Based on the Lemma 2 we can get that \( \sum\nolimits_{j = 1}^{u} {P_{GA} } \left( {B/A_{j}^{\left( 1 \right)} } \right)P\left( {A_{j}^{\left( 1 \right)} } \right) \ge \sum\nolimits_{j = 1}^{u} {P_{GA} } \left( {B/A_{j}^{\left( 0 \right)} } \right)P\left( {A_{j}^{\left( 0 \right)} } \right) \), so we can have
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Li, T., He, T., Wang, Z. et al. SDF-GA: a service domain feature-oriented approach for manufacturing cloud service composition. J Intell Manuf 31, 681–702 (2020). https://doi.org/10.1007/s10845-019-01472-1
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DOI: https://doi.org/10.1007/s10845-019-01472-1