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QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system

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  • Feng Xiang
  • Yefa Hu
  • Yingrong Yu
  • Huachun Wu

Abstract

Service composition and optimal selection (SCOS) is one of the key issues for implementing a cloud manufacturing system. Exiting works on SCOS are primarily based on quality of service (QoS) to provide high-quality service for user. Few works have been delivered on providing both high-quality and low-energy consumption service. Therefore, this article studies the problem of SCOS based on QoS and energy consumption (QoS-EnCon). First, the model of multi-objective service composition was established; the evaluation of QoS and energy consumption (EnCon) were investigated, as well as a dimensionless QoS objective function. In order to solve the multi-objective SCOS problem effectively, then a novel globe optimization algorithm, named group leader algorithm (GLA), was introduced. In GLA, the influence of the leaders in social groups is used as an inspiration for the evolutionary technology which is design into group architecture. Then, the mapping from the solution (i.e., a composed service execute path) of SCOS problem to a GLA solution is investigated, and a new multi-objective optimization algorithm (i.e., GLA-Pareto) based on the combination of the idea of Pareto solution and GLA is proposed for addressing the SCOS problem. The key operators for implementing the Pareto-GA are designed. The results of the case study illustrated that compared with enumeration method, genetic algorithm (GA), and particle swarm optimization, the proposed GLA-Pareto has better performance for addressing the SCOS problem in cloud manufacturing system. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Feng Xiang & Yefa Hu & Yingrong Yu & Huachun Wu, 2014. "QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(4), pages 663-685, December.
    • Handle: RePEc:spr:cejnor:v:22:y:2014:i:4:p:663-685
    DOI: 10.1007/s10100-013-0293-8
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    References listed on IDEAS

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    1. Elkins, Debra A. & Huang, Ningjian & Alden, Jeffrey M., 2004. "Agile manufacturing systems in the automotive industry," International Journal of Production Economics, Elsevier, vol. 91(3), pages 201-214, October.
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    Cited by:

    1. Wei He & Guozhu Jia & Hengshan Zong & Jili Kong, 2019. "Multi-Objective Service Selection and Scheduling with Linguistic Preference in Cloud Manufacturing," Sustainability, MDPI, vol. 11(9), pages 1-15, May.
    2. Wei He & Guozhu Jia & Hengshan Zong & Tao Huang, 2019. "Multi-Objective Cloud Manufacturing Service Selection and Scheduling with Different Objective Priorities," Sustainability, MDPI, vol. 11(17), pages 1-24, September.
    3. Shuai Zhang & Yangbing Xu & Wenyu Zhang & Dejian Yu, 2019. "A new fuzzy QoS-aware manufacture service composition method using extended flower pollination algorithm," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2069-2083, June.
    4. Ali Salmasnia & Zahra Kiapasha & Melika Pashaeenejad, 2024. "Subtasks scheduling of tasks with different structures in cloud manufacturing systems under maintenance policy and focusing on logistics, tardiness, and earliness aspects," Operational Research, Springer, vol. 24(3), pages 1-37, September.
    5. Haghnegahdar, Lida & Chen, Yu & Wang, Yong, 2022. "Enhancing dynamic energy network management using a multiagent cloud-fog structure," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).

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