Globalization, servitization, and customization in the marketplace are changing the way manufacturing enterprises do their business. Cloud manufacturing (CMfg) offers a possibility to perform large-scale manufacturing collaboration. However, CMfg systems are immature in many aspects. Service selection and scheduling is a key issue for practical implementation of CMfg. In this paper, a service selection and scheduling model is established, with criteria TQCS (time, quality, cost, and service) being considered. A fuzzy decision-making theory is adopted to transform TQCS values into relative superiority degrees. This is different from the traditional linear weighted method in most previous research, which results in large values of non-standardization error. The four relative superiority degrees are then combined linearly into an overall objective, in which the weight coefficients are calculated through analytic hierarchy process (AHP). Afterwards, ant colony optimization (ACO) is repurposed for the established service selection and scheduling model. Meanwhile, a selection mechanism is added to ACO (now ACOS) to enhance its validity. The simulation results demonstrate the practicality of the proposed model and the effectiveness of ACOS compared with other widely used algorithms.

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The development of new generation information technology has brought opportunities for industrial production model innovation. Especially, the cloud computing, Internet of Things, and big data technology are widespread applied in industrial fields. Based on this tendency, a service-oriented networked manufacturing model called cloud manufacturing (CM) was proposed in 2010. In order to realize this manufacturing model, one of the key technologies is how to achieve the discovery of manufacturing service which has not found a suitable solution. In this paper, a manufacturing service discovery framework based on agent is provided. The architecture consists of two parts: one is that manufacturing task agent and manufacturing service agent based on the expansion of the object model, and the other one is task and service matching process knowledge base. Furthermore, a structural matching method is proposed to implement the static parameters matching of task agent and service agent, and a multi-agent system bid mechanism is built to accomplish the dynamic parameters matching of the two agents. A simulation environment based on JADE has been properly developed. The simulation shows that the discovery method can effectively achieve the manufacturing service discovery in CM environment, which provides technical support for the cloud manufacturing service platform development.