Industry 4.0 focuses on the realization of smart manufacturing from shop floors to factories and to the whole supply chain. As a key technology of smart manufacturing, cyber-physical system has been widely discussed in the aspects of system design, data collection and processing, and cyber-physical synchronization. In a smart shop floor, manufacturing resources with intelligence and autonomy are abstracted as cyber-physical system units. They can communicate with each other autonomously to make optimal production decisions according to the real-time status of the shop floor. In this article, an autonomous collaboration network comprised of cyber-physical system–based smart manufacturing resources is modeled by using complex network theory. The collaboration activities among them are further analyzed, from which the information of key cyber-physical system units and key collaboration relationships are excavated. A demonstrative case is studied to verify the feasibility of the proposed model. From the case, it can be seen that (1) autonomous collaboration network has a small-world feature; (2) cyber-physical system units with bigger degrees and the collaborative relationships with bigger tightness are more important; (3) the workload of cyber-physical system units needs to be balanced because some cyber-physical system units have exceeded their capacities; and (4) cyber-physical system units with larger collaboration clustering coefficients will attract other nodes to form communities centered by them. Based on these results, the autonomous production control and management of smart shop floor will become more accurate, efficient, and balanced.

In the context of service-oriented manufacturing, warehouse product service system (WPSS) has been proposed through integrating smart warehouse and ‘centralized purchase + shared storage + JIT distribution’ services for various enterprises in an Industrial Park. The influence mechanism of enterprises‘ uncertain demand on profit decision of WPSS was deduced. Firstly, a mathematical model was established by turning enterprises’ uncertain demand into service cost. Its maximum profit was taken as the object, considering the storage income, penalty income for delayed storage, and distribution cost. Then, an improved interval number optimization method with midpoint and width was adopted to optimize the possibility of maximum profit; its possibility was used to quantify the decision makers’ tolerance for uncertain demand. Finally, a case study was presented to verify the feasibility of the proposed method. It is expected that the maximum profit under enterprises’ uncertain demand can provide decision support for resources configuration and operation scheduling of WPSS.

In order to conduct resource sharing and deployment in cloud manufacturing environment, a concept of collaborative manufacturing chain was proposed. Based on machining tasks with the sequential characteristics, the proposed model considering the criteria of service cost, service time, service quality and service utilization was constructed. Fuzzy analytical hierarchy process was adopted to add the above multi-criteria model to a single objective problem. Then, an improved firefly algorithm was used to solve a reasonable collaborative manufacturing chain scheme. Based on the discrete characteristics of the collaborative manufacturing chain, iterative position function was improved to make the solution space to be a discrete domain. Furthermore, particle swarm optimization was used to optimize the step length factor α, attraction degree β₀ and light absorption coefficient γ so as to prevent the firefly algorithm from local optimum. Compared with the genetic algorithm, numerical result suggests that the improved firefly algorithm has more advantages in convergence speed and solving efficiency. It is expected that this study can provide a useful reference for the service composition of collaborative manufacturing chain.

Under the background of the fourth industrial revolution driven by the new generation information technology and artificial intelligence, human–robot collaboration has become an important part of smart manufacturing. The new “human–robot–environment” relationship conducts industrial robots to collaborate with workers to adapt to environmental changes harmoniously. How to determine a reasonable human–robot interaction operations allocation strategy is the primary problem, by comprehensively considering the workers’ flexibility and industrial robots’ automation. In this paper, a human–robot collaborative operation framework based on CNC (Computer Number Control) machine tool was proposed, which divided into three stages: pre-machining, machining and post-machining. Then, an action-based granularity decomposition method was used to construct the human–robot interaction hierarchical model. Further, a collaboration effectiveness-based operations allocation function was established through normalizing the time, cost, efficiency, accuracy and complexity of human–robot interaction. Finally, a simulated annealing algorithm was adopted to solve preferable collaboration scheme; a case was used to verify the feasibility and effectiveness of the proposed method. It is expected that this study can provide useful guidance for human–robot interaction operations allocation on CNC machine tools.