A matching-synergy 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 two-constraint 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.

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“…Some scholars have established relevant multi-objective combination models from different perspectives [16][17][18]. In addition to studying fundamental attribute indicators such as time and cost, some scholars have also established models that include indicators such as service credibility and reliability [19][20][21][22]. Viriyasitavat et al [23] advocated blockchain technology to transfer and validate the trustworthiness of services and partners.…”