Spatial-temporal aware service composition for production factors under industrial internet

Industrial Internet is a promising technology combining industrial systems with Internet techniques to significantly improve production efficiency and reduce cost by cooperating with intelligent devices. Under industrial internet environment, a production process usually consists of multiple subtasks, and one or more types of product factors are needed to finish a subtask. Thus, the service composition under industrial application is more complex and challenging compared with traditional service composition under the Internet environment. In this article, we model the problem of service composition for production factors under industrial internet as a multiobjective optimization problem. To derive the optimal Pareto service composition plans, we propose a hybrid optimization algorithm, named TLBO-TS, by combining the advantages of teaching-learning-based optimization algorithm and tabu search algorithm. 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.