Semantic knowledge-driven A-GASeq: A dynamic graph learning approach for assembly sequence optimization

Xia, Luyao; Lu, Jianfeng; Lu, Yuqian; Gao, Wentao; Fan, Yuhang; Xu, Yuhao; Zhang, Hao

doi:10.1016/j.compind.2023.104040

Cited by 2 publications

(1 citation statement)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assembly parts are diverse, and the processes are complex. There is a significant probability of uncertainties such as delayed material deliveries, labor short-ages, and equipment damage [34][35][36]. Therefore, ensuring the continuous fulfillment of production tasks in the workshop becomes a crucial issue.…”

Section: Introductionmentioning

confidence: 99%

An Assembly Sequence Planning Method Based on Multiple Optimal Solutions Genetic Algorithm

Wan,

Liu,

Qiu

et al. 2024

Mathematics

View full text Add to dashboard Cite

Assembly sequence planning (ASP) is an indispensable and important step in the intelligent assembly process, and aims to solve the optimal assembly sequence with the shortest assembly time as its optimization goal. This paper focuses on modular cabin construction for large cruise ships, tackling the complexities and challenges of part assembly during the process, based on real engineering problems. It introduces the multiple optimal solutions genetic algorithm (MOSGA). The MOSGA analyzes product constraints and establishes a mathematical model. Firstly, the traditional genetic algorithm (GA) is improved in the case of falling into the local optimum when facing complex problems, so that it can jump out of the local optimum under the condition of satisfying the processing constraints and achieve the global search effect. Secondly, the problem whereby the traditional search algorithm converges to the unique optimal solution is solved, and multiple unique optimal solutions that are more suitable for the actual assembly problem are solved. Thirdly, for a variety of restrictions and emergencies that may occur during the assembly process, the assembly sequence flexible planning (ASFP) method is introduced so that each assembly can be flexibly adjusted. Finally, an example is used to verify the feasibility and effectiveness of the method. This method improves the assembly efficiency and the diversity of assembly sequence selection, and can flexibly adjust the assembly sequence, which has important guiding significance for the ASP problem.

show abstract

Section: Introductionmentioning

confidence: 99%