In Internet era, customer requirements reveals new characteristics, such as diversification, personalization and complex products. For example smart connected products such as smart watch, wearable devices, air conditioning equipment connect to internet are composed of physical, smart, connectivity components (Porter and Heppelmann, 2014). Additionally, these products are generally provided as services which requires large scale collaboration among geographically distributed developers, support personal and infrastructure. Enterprises with limited resources and capacity cannot independently construct the complete manufacturing value chain and transform raw materials into end products (Azevedo et al., 2017). Thus, it is necessary to collaborate with others for complementary resources to build business process to seize these emerging market opportunities. As services become the predominant vehicle of resource provisioning in industries, the selection of the most suitable services and their composition to support business work-flow based on dynamic alliances becomes extremely important. A muti-subject, cross-organization and cross-region collaborative design and manufacturing process needs to be automatically composed and a dynamic task-driven temporary cooperation alliance needs to be created with online services. Current research has explored various criteria and models for service composition in the context of Service Oriented Architecture (SOA) (Jula et al. 2014). They primarily use functional characteristics and Quality of Service (QoS) attributes as service selection criteria (Zhang et al. 2016; Lu and Xu 2017). These approaches generally consider each service as independent which needs to complete the sub-task assigned to it satisfactorily. However, in cloud environment large number of services that provide same or similar functions and has similar QoS attributes may be available which makes service selection more difficult. Additionally, there exist some dependent relationship constraints among complex sequential sub-tasks, such as resource logistics, and information and knowledge delivery (Castro et al. 2013), which bring out the synergy requirements for better service performance. In practice, service groups corresponding to the complex task should consider collaboration factors, and must not only have the requisite qualifications and satisfied QoS, but also own high-level synergy effect in order to ensure resources, information effective hand-off and smooth flow of sub-tasks. Moreover, high level collaboration will reduce the time and cost of coordination and promote the manufacturing efficiency and reliability (Liu et al. 2016).