Energy-efficient collaborative scheduling of heterogeneous multi-stage hybrid flowshop for large metallic component manufacturing

Abstract

Green manufacturing is attracting more attention. The manufacturing of large metallic components is high-energy-consuming and high-polluting. It usually involves heterogeneous processes: such as cutting stock, welding assembly, and machining, and produces a great number of carbon emissions. In addition, large metallic components have many specifications of parts, which is prone to the problem of kitting. According to the manufacturing process characteristics of large metallic components and considering the turn on/off strategy of machines, a collaborative scheduling model aiming at minimizing makespan and carbon emissions is constructed. In order to find the Pareto optimal solutions of the problem, an improved NSGA-II combined with the moth-flame optimization algorithm (NSGA–II–MFO) is proposed. Finally, examples with different scales are designed to evaluate the effectiveness of the mathematical model and algorithm. Furthermore, the proposed model and algorithm are also verified on a real-world case from a multi-stage manufacturing workshop in a Chinese excavator factory, which produces a series of excavator boom. The results show that the collaborative scheduling of multi-stage manufacturing flowshop for large metallic components is significantly better than the non-collaborative workshop. At the same time, comparing with 3 other multi-objective optimization algorithms, it also demonstrates the effectiveness of the proposed algorithm in solving such problems.