AN IDENTICAL HARMONY DEGREE-BASED OPTIMIZATION SELECTION METHOD FOR CLOUD MANUFACTURING SERVICE COMPOSITION

Abstract

In a cloud manufacturing scenario, the
matching degree between manufacturing tasks and cloud
services, as well as the synergy degree between multiple
selected cloud services are key metrics to measure the benefits
of cloud manufacturing service application. However, the
latter is often overlooked in the cloud service selection
process. In this paper, a matching-synergy analysis-based
optimization
method of cloud manufacturing service composition is
proposed. Firstly, an evaluation system of cloud service
composition quality is established, including service matching
degree (SM), service composition synergy (CS), and other
metrics, such as service time (T), service cost (C) and
reliability (R). Secondly, considering the interests of both
service requestors and service resource providers, a twoconstraint
combination preference model is constructed, and
solved by using the improved ant colony algorithm (IACO).
Finally, the feasibility and effectiveness of the proposed
method are verified with the example of an automobile
bumper cloud service.