Collaboration-Aware Service Composition Optimization for Production Factors Under Industrial Internet

The industrial Internet integrates industrial systems with Internet technologies, significantly enhancing production efficiency and reducing costs through collaboration with intelligent devices. Under the context of the industrial Internet, a production process typically comprises multiple tasks, each task requiring one or more types of production factors, which makes the service composition of production factors more complex and challenging compared to the traditional service composition under Web environments. To optimize the service composition of production factors under industrial Internet, this paper proposes a collaboration-aware service composition optimization model by considering the collaboration relationships among services. Specifically, both historical collaboration and present collaboration are considered, where the historical collaboration relationships among services include the collaboration between two directly-followed tasks and collaboration within one task, and the present collaboration relationships include the collaboration among the selected services in present service composition. To derive the optimal service composition plan, we propose using the advanced teaching-learning-based optimization algorithm (TLBO). Extensive experiments are conducted to compare with other population-based optimization methods under a real-world ship production process to verify the superiority of our approach. Experimental results show that the integration of collaboration relationships improves the overall quality of the optimized service composition plans in terms of time and cost, and the selected TLBO is more effective to derive the optimized service composition plans than other population-based optimization methods.