Abstract: To address the problems of long manufacturing cycles and high cost caused by the large number and complex decomposition of manufacturing tasks in a cloud environment, a composition method of cloud manufacturing subcontracting services is proposed. The method decomposes tasks into multiple sub-tasks that can be executed in parallel, and reduces production cycles and cost by introducing more manufacturing resources and improving market competitiveness. In order to efficiently solve the optimization model of cloud manufacturing subcontracting service composition, an improved tunicate swarm algorithm is proposed. First, the current information of individuals is shared, and the locations of surrounding individuals are integrated when performing group behavior, so as to improve the local exploitation ability of the algorithm; second, individual historical information is shared, and individuals explore the historical optimal location of others to improve the global exploration ability of the algorithm; finally, the global exploration and local exploitation behavior of the population is adjusted adaptively according to the current state to improve the stability of the algorithm. Through simulation experiments, it is verified that the proposed scheme is superior to the comparative one in controlling the time and cost of large-scale manufacturing tasks.

Key words: cloud manufacturing, subcontract, service composition, tunicate swarm algorithm (TSA)