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An Improved Grey Wolf Optimizer Algorithm for Energy-Aware Service Composition in Cloud Manufacturing

  • Yefeng Yang
  • Bo YangEmail author
  • Shilong Wang
  • Wei Liu
  • Tianguo Jin
ORIGINAL ARTICLE
  • 18 Downloads

Abstract

Green and sustainable manufacturing is an inevitable development trend of manufacturing in the future, wherein low-energy consumption is regarded as a significant component. As an emerging manufacturing service system, cloud manufacturing (CMfg) is characterized by wide distribution, large quantity and complicated calling method for manufacturing services. Therefore, the energy consumption in the execution of composition service should be considered. However, few works related to energy consumption are focused on service composition at present. In response, this paper proposes an energy-aware service composition and optimal selection (EA-SCOS) model to ensure high-quality and low-energy consumption during the tasks. The mathematical model of energy consumption based on quality of service (QoS) is established, wherein the evaluation methods for manufacturing energy consumption and logistics energy consumption are described in detail. In order to solve the EA-SCOS problem effectively, a state-of-the-art algorithm which has been successfully applied in other fields, named grey wolf optimizer (GWO), is introduced. In addition, two key improvements for GWO are proposed to ensure the accuracy of solution. Finally, the effectiveness and feasibility of improved GWO (IGWO) are verified by a comparative study with GWO, genetic algorithm (GA) and max–min ant system (MMAS). Simulation results show that the proposed model is valid for reducing service energy consumption. Moreover, the improved strategies have obvious, positive effect on the accuracy of solutions and the performance of IGWO for addressing EA-SCOS problem is obviously better than the other three algorithms.

Keywords

Cloud manufacturing Service composition Energy consumption Grey wolf optimizer 

Notes

Funding information

The presented work was supported by the Key Technologies Research and Development Program of China (no. 2018AAA0101804), Fundamental Research Funds for the Central Universities (2018CDQYJX0013), the National Defense Basic Research Project of China (no. JCKY2016204A502), and the open research fund project of state key laboratory of complex product intelligent manufacturing system technology (grant number QYYE602).

Supplementary material

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Yefeng Yang
    • 1
  • Bo Yang
    • 1
    Email author
  • Shilong Wang
    • 1
  • Wei Liu
    • 2
  • Tianguo Jin
    • 3
  1. 1.State Key Laboratory of Mechanical TransmissionChongqing UniversityChongqingChina
  2. 2.State Key Laboratory of Complex Product Intelligent Manufacturing System TechnologyCASICBeijingChina
  3. 3.School of Mechatronics EngineeringHarbin Institute of TechnologyHarbinChina

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