Sustainable‐collaborative scheduling of multi‐stage hybrid flowshop with heterogeneous production and transportation using a green scheduling strategy‐based NSGA‐II‐MFO algorithm

Abstract The manufacturing of large metallic components typically involves cutting, welding, assembly, and machining, which consume a significant number of resources and result in substantial carbon emissions. In addition, there is a wide variety of intermediate parts in the production process, which is prone to the problem of kitting and long waiting times. The traditional single‐stage scheduling made it difficult to achieve overall production optimization. Furthermore, due to the large size of the workpiece, transportation equipment also consumes a significant amount of energy and generates carbon emissions during transportation. In heterogeneous hybrid flowshop, there is significant potential for optimizing the collaborative scheduling between processing machines and transportation equipment as well as between different transportation devices. This study presents a green scheduling model that considers the optimization of both the maximum makespan and the carbon emissions generated by the processing machines and transport equipment. The model also takes into account the idle state of the processing machines to further reduce carbon emissions. A green scheduling strategy is proposed to solve this model, along with an enhanced NSGA‐III (Non‐Dominated Sorting Genetic Algorithm III) that integrates the Moth‐Flame Optimization algorithm. Additionally, 15 arithmetic examples are provided to illustrate the manufacturing process of large metallic components of different scales. The effectiveness of the proposed algorithm is demonstrated through comparisons with commonly used intelligent optimization algorithms and non‐collaborative scheduling. The findings highlight the efficacy of collaborative scheduling in the heterogeneous multi‐stage hybrid flowshop for large metallic components, resulting in reduced manufacturing time and carbon emissions.