An HTCPN-Based Self-Adaptive Optimal Control Method for Multi-Level Collaborative Manufacturing Networks

The increasing demand for highly customized complex products, such as aircraft and aero engines, in dynamic global production environments has brought great challenges to manufacturing enterprises and supply networks. To tackle the problem of dynamics and networked control among distributed heterogeneous manufacturing resources, a hierarchical timed colored Petri net (HTCPN)-based self-adaptive optimal control (SOC) method is proposed for multi-level collaborative manufacturing networks. In contrast to existing HTCPN models, an industrial dataspace is designed to interoperate large-scale, multi-source, and heterogeneous real-time data, which provides manufacturing processes with data subspaces dynamically. To achieve SOC, the corresponding optimization problem is solved by a tailored multi-objective ant colony optimization (ACO) algorithm. A case study based on a Chinese aircraft manufacturer demonstrates the effectiveness and efficiency of the proposed method in reducing cost, time, and energy consumption. This paper potentially enables discrete manufacturing enterprises to implement SOC in multi-level manufacturing networks.