Urgent task-aware cloud manufacturing service composition using two-stage biogeography-based optimisation

ABSTRACT

Optimal service composition is a critical issue in cloud manufacturing systems. However, unpredictable dynamic factors, such as urgent task request occurring during the production process will have an influence on the original execution plan. In this paper, a new multi-objective optimisation problem based on quality of service of urgent task-aware cloud manufacturing service composition is presented, and two service recomposition methods based on vertical collaboration and speed selection are proposed to expedite task completion. A two-stage (i.e. composition and recomposition) biogeography-based optimisation (TBBO) algorithm is proposed to solve the corresponding model. Variable neighbourhood search is used to improve the exploitation ability of the TBBO algorithm. Experiment results demonstrate that the proposed TBBO algorithm can obtain better cloud manufacturing service composition and recomposition solutions compared with the basic biogeography-based optimisation algorithm, genetic algorithm, and differential evolution, and the two proposed service recomposition methods can reduce task execution time more effectively than the original composition method.

KEYWORDS:

• Cloud manufacturing service composition
• urgent task request
• service recomposition
• biogeography-based optimisation algorithm

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by National Natural Science Foundation of China [51375429, 51475410], Zhejiang Natural Science Foundation of China [LY17E050010], and Humanities and Social Sciences Research Project of Ministry of Education, China [15YJC870008].