Knowledge-Driven Scheduling of Digital Twin-Based Flexible Ship Pipe Manufacturing Workshop

Pipe manufacturing is an essential phase in ship construction. Aiming at the problems of insufficient dynamic response-ability, human-oriented workshop control, and unbalanced equipment load in ship pipe manufacturing workshops, a knowledge-driven scheduling method for flexible ship pipe manufacturing workshops is proposed to comprehensively improve the efficiency of ship pipe manufacturing. A digital twin-enabled scheduling framework for flexible ship pipe manufacturing workshops is designed. On this basis, an ontology model of ship pipe manufacturing is described by analyzing the data of the ship pipe manufacturing workshop. And according to the low-volume and multi-variety manufacturing demand, a knowledge-driven multi-objective optimal shop scheduling model is constructed based on the equipment selection rules and scheduling optimization rules produced by ontology knowledge inference. Then, an improved Multi-objective Evolutionary Algorithm Based on Decomposition (IMOEA/D) is proposed. Finally, a case study in a pipe machining production line is studied to validate the proposed approach and the simulations show that the proposed approach is superior to other scheduling algorithms and decision makers can obtain more effective production execution plans.