Graph-based assembly sequence planning algorithm with feedback weights

Zhu, Xiaojun; Xu, Zhigang; Wang, Junyi; Yang, Xiao Yi; Fan, Linlin

doi:10.1007/s00170-022-10639-9

Cited by 7 publications

(2 citation statements)

References 40 publications

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“…Once again, flexible planning was conducted, and the resulting sequence is shown in the third row of Table 7. The optimized assembly sequence is as follows: 1, 2, 4, 9, 3, 5, 15,14,6,7,11,10,13,12,8. This sequence meets the constraints imposed by the already assembled parts, 1, 4, 9, 3, 5, 15, 14, 6, 7, and ensures that part 8 is assembled at a later time to address the damage issue.…”

Section: Asfp Case Studymentioning

confidence: 99%

“…ASP is a complex optimization challenge that typically entails a vast array of possible sequences and constraints. In solving such problems, GAs hold several advantages over other approaches like HA, ACO, PSO, and SA: Firstly, GAs sustain population diversity through crossover and mutation operations, enhancing the comprehensive exploration of the solution space and reducing the likelihood of converging to local optima [15]. Secondly, GAs do not directly use the actual values of decision variables for optimization calculations but operate on them in encoded form.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Wan,

Liu,

Qiu

et al. 2024

Mathematics

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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.

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