| Service Computing |
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| Optimization of massive O2O service composition |
| ZHANG Li-Na, YU Yang |
| 1. School of Information Engineering, Quzhou College of Technology, Quzhou Zhejiang 324000, China；
2. School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510000, China |
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Abstract The social relation theory was introduced under the environment of massive online to offline (O2O) services, to improve the collaborative efficiency between service providers at online service composition stage, and to optimize algorithm execution efficiency. Firstly, a social relation network was constructed which could reflect the collaboration efficiency of service providers. Secondly, service filtration stage was added before online optimization, and a filtration method called social relation-expanded Skyline filtering was proposed to increase service composition execution efficiency under massive-service environment. Finally, social-expanding genetic operators were added to multi-objective genetic algorithm to improve the collaboration efficiency in stage of service composition. Experimental results show that the collaboration efficiency between service providers is enhanced with a tiny service quality loss using this composition service optimization method. And the convergence rate of service composition algorithm is also improved under the environment of massive O2O services．
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Published: 11 June 2017
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Cite this article:
ZHANG Li-Na, YU Yang. Optimization of massive O2O service composition. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(6): 1259-1268.
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海量O2O服务组合的优化
在解决海量线上到线下(O2O)服务环境中,引入社会关系理论,在线上服务组合阶段考虑提高线下服务提供商之间的协作效率,同时优化算法的执行效率.首先,建立能够反映线下服务供应商之间协作效率的社会关系网络模型；其次,在线上优化阶段前增加服务过滤阶段,提出社会关系扩展的Skyline过滤方法,提高海量服务环境下的组合服务执行效率；最后,在服务组合优化阶段,在多目标遗传算法中增加针对协作效率的局部搜索算子.实验结果表明,该服务组合优化方法以极小的服务质量损失为代价,提高了服务提供商之间的协作效率,同时提升了海量O2O服务环境中服务组合算法的收敛速度.
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