A decision-making method for assembly sequence planning with dynamic resources

Wu, Wenbo; Huang, Zhengdong; Zeng, Jiani; Fan, Kuan

doi:10.1080/00207543.2021.1937748

Cited by 12 publications

(5 citation statements)

References 33 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 NP-hard problem [4], particularly challenging when dealing with a large number of product parts, leading to a phenomenon known as "combinatorial explosion", which significantly increases the difficulty of solving the problem [5]. Numerous scholars have employed various search algorithms, such as genetic algorithms (GA), ant colony optimization (ACO), heuristic algorithms (HA), simulated annealing (SA), and particle swarm optimization (PSO), to tackle the ASP [6][7][8][9][10][11]. Tseng, H.E.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Asfp Case Studymentioning

confidence: 99%

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

show abstract

“…The outcomes of applying to various industrial goods are compared with well-known optimization methods that have already been developed, such as the genetic algorithm and ant colony optimization algorithm. In [20], a deep reinforcement learning (DRL) approach is used to tackle the Assembly sequence planning (ASP) problem. This approach seeks to speed up response times by utilizing the reusability and expandability of earlier decision-making experiences.…”

Section: Introductionmentioning

confidence: 99%

Generation of robotized assembly order using Liaison and Matrix methods: A comparative study

Dash,

Sahu,

Dash

2024

Preprint

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A robotic assembly process must be properly developed to achieve the highest throughput, viability, and efficiency. A great variety of ways to order creation have been documented in various literatures, each with its own methodology. However, the majority of literature employs soft computing strategies to generate assembly orders. This paper compares two order generating approaches, the liaison method and the matrix method. The matrix method offers a wide variety of possible applications. This technique may be implemented into the robot motion control software and is also simpler to add into automation operations. This technique has a high degree of convergence and uniqueness. However, the building of matrices must be done properly in order to obtain the correct result. The liaison method employs a logical approach via a series of inquiries that result in the desired precedence connection among the components. Assembly orders are generated using precedence relationships. The success of this strategy is dependent on the responses to a set of questions sent to each liaison. This method's appropriateness is related to items that have fewer components. The two approaches that have been chosen are deliberated about and applied to arbitrarily chosen items, which serve as the basis for the creation of a fundamental and correct approach for the production of robotic assembly orders. The study demonstrates that robotic assembly cells may use the matrix technique. The goal of the ongoing effort is to increase the robotic assembly system's capacity and adaptability.

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“…This will allow, in principle, to increase the marginal utility of the product and lower the prices obtaining benefits in its commercialization. Studies have determined that, on average, the costs associated with the production of an assembled product can be between 40% and 50% of the total manufacturing cost, and in the same percentages, up to the total production time of a product [4].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Assembly Sequence Planning (CASP) based on data analysis for On-site Installation Products

Vélez,

Uribe,

Aristizabal

et al. 2023

Preprint

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For the manufacture of significantly repetitive products that require assembly, different strategies have been proposed to improve times, the use of resources, and the reduction of associated costs. For this purpose, there are many documented methodologies that point to method and time studies, reduction of the number of components or the complexity of the assembly as the \acrfull{DFA}, sequence analysis with \acrfull{ASP}, Bourjault, and/or graphs, among others.In the case of products that require to be assembled at the site where they will operate, manufacturing in a fixed position, and of which several assemblies will be performed in different places, the execution of these has been done, almost always, in an intuitive way, that is, solving on the fly, starting from common sense and accumulating parts. The existing literature has limited information on the methods developed for assembly with these characteristics.This study presents a new methodology of sequence generation called \acrfull{CASP} for the on-site installation of a product in which, the criteria and knowledge of a group of engineers are considered, helping to identify the assembly operations, the sequences, the arrangement and allocation of resources and tasks for the development of the assembly.Additionally, this methodology is verified in a case study applied to a photovoltaic charging station, for which a feasible, effective, detailed, and structured assembly sequence was collaboratively generated for the assembly of such a product. The results show the applicability of the \Gls{CASP} methodology and suggest that it could be applied in other complex product assembly contexts requiring on-site assembly.

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A decision-making method for assembly sequence planning with dynamic resources

Cited by 12 publications

References 33 publications

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

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

Generation of robotized assembly order using Liaison and Matrix methods: A comparative study

Collaborative Assembly Sequence Planning (CASP) based on data analysis for On-site Installation Products

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