Modelling of assembly sequence planning problem using base part concept

2018 ◽  
Author(s):  
M. A. Abdullah ◽  
M. F. F. Ab Rashid ◽  
Z. Ghazalli ◽  
N. M. Z. Nik Mohamed ◽  
A. N. Mohd Rose
Keyword(s):  
Assembly Sequence Planning ◽  
Planning Problem ◽  
Assembly Sequence ◽  
Sequence Planning

scholarly journals Knowledge-Based Approach to Assembly Sequence Planning for Wind-Driven Generator

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Meiping Wu ◽  
Yi Zhao ◽  
Chenxin Wang
Keyword(s):  
Manufacturing Industry ◽  
Assembly Sequence Planning ◽  
Product Launch ◽  
Assembly Planning ◽  
Planning Problem ◽  
Efficient Computation ◽  
Assembly Sequence ◽  
Sequence Planning ◽  
Novel Approach ◽  
The Right

Assembly sequence planning plays an essential role in the manufacturing industry. However, there still exist some challenges for the research of assembly planning, one of which is the weakness in effective description of assembly knowledge and information. In order to reduce the computational task, this paper presents a novel approach based on engineering assembly knowledge to the assembly sequence planning problem and provides an appropriate way to express both geometric information and nongeometric knowledge. In order to increase the sequence planning efficiency, the assembly connection graph is built according to the knowledge in engineering, design, and manufacturing fields. Product semantic information model could offer much useful information for the designer to finish the assembly (process) design and make the right decision in that process. Therefore, complex and low-efficient computation in the assembly design process could be avoided. Finally, a product assembly planning example is presented to illustrate the effectiveness of the proposed approach. Initial experience with the approach indicates the potential to reduce lead times and thereby can help in completing new product launch projects on time.

An Ant Colony Algorithm for Integrating Assembly Sequence Planning and Assembly Line Balancing

2013 ◽  
Vol 397-400 ◽  
pp. 2570-2573 ◽  
Author(s):  
Zhuo Yang ◽  
Cong Lu ◽  
Hong Wang Zhao
Keyword(s):  
Ant Colony Algorithm ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Assembly Sequence Planning ◽  
Ant Colony ◽  
Line Balancing ◽  
Assembly Planning ◽  
Planning Problem ◽  
Assembly Sequence ◽  
Sequence Planning

Assembly sequence planning (ASP) and assembly line balancing (ALB) problems are two essential problems in the assembly optimization. This paper proposes an ant colony algorithm for integrating assembly sequence planning and assembly line balancing, to deal with the two problems on parallel, and resolve the possible conflict between two optimization goals. The assembly sequence planning problem and the assembly line balancing problem are discussed, the process of the proposed ant colony algorithm is investigated. The results can provide a set of solutions for decision department in assembly planning.

A hybrid Ant-Wolf Algorithm to optimize assembly sequence planning problem

2017 ◽  
Vol 37 (2) ◽  
pp. 238-248 ◽  
Author(s):  
Mohd Fadzil Faisae Ab Rashid
Keyword(s):  
Assembly Sequence Planning ◽  
Search Direction ◽  
Local Optimum ◽  
Planning Problem ◽  
Gray Wolf ◽  
Assembly Sequence ◽  
Algorithm Performance ◽  
Content Type ◽  
Sequence Planning ◽  
Updating Procedure

Purpose This paper aims to optimize the assembly sequence planning (ASP) problem using a proposed hybrid algorithm based on Ant Colony Optimization (ACO) and Gray Wolf Optimizer (GWO). The proposed Hybrid Ant-Wolf Algorithm (HAWA) is designed to overcome premature convergence in ACO. Design/methodology/approach The ASP problem is formulated by using task-based representation. The HAWA adopts a global pheromone-updating procedure using the leadership hierarchy concept from the GWO into the ACO to enhance the algorithm performance. In GWO, three leaders are assigned to guide the search direction, instead of a single leader in most of the metaheuristic algorithms. Three assembly case studies used to test the algorithm performance. Findings The proposed HAWA performed better in comparison to the Genetic Algorithm, ACO and GWO because of the balance between exploration and exploitation. The best solution guides the search direction, while the neighboring solutions from leadership hierarchy concept avoid the algorithm trapped in a local optimum. Originality/value The originality of this research is on the proposed HAWA. In addition to the standard pheromone-updating procedure, a global pheromone-updating procedure is introduced, which adopted leadership hierarchy concept from GWO.

Optimal robotic assembly sequence planning using stability graph through stable assembly subset identification

2019 ◽  
Vol 233 (15) ◽  
pp. 5410-5430 ◽  
Author(s):  
G Bala Murali ◽  
BBVL Deepak ◽  
MVA Raju ◽  
BB Biswal
Keyword(s):  
Research Work ◽  
Search Space ◽  
Assembly Sequence Planning ◽  
Robotic Assembly ◽  
Planning Problem ◽  
Assembly Sequence ◽  
Sequence Planning ◽  
Fitness Value ◽  
Assembly Sequences ◽  
Stability Graph

Assembly sequence planning is one of the multi-model optimization problems, in which more than one objective function has to be optimized at a time to obtain the quality assembly sequence. Moreover obtaining the feasible sequences from the possible finite set of sequences is a difficult task as the assembly sequence planning problem is N-P hard combinatorial problem. To solve the assembly sequence planning problem, researchers have developed various techniques to obtain the optimum solution. The developed methodologies have many drawbacks like struck at local optima, poor performance, huge search space and many more. To overcome these difficulties, the current research work aims to use stability graph to generate stable assembly subsets for obtaining the optimum assembly sequences. In the proposed methodology, to reduce the search space and to obtain the quality assembly sequences, stability graph is considered. Moreover, the fitness of assembly subsets is evaluated according to the user weights at each level before proceeding to the higher levels. Due to this, the higher fitness value subsets are eliminated at each stage by which time of execution will reduce enormously. The proposed methodology has implemented on various industrial products and compared the results with the various well-known algorithms.

A new discrete double-population firefly algorithm for assembly sequence planning

2016 ◽  
Vol 230 (12) ◽  
pp. 2229-2238 ◽  
Author(s):  
Zaifang Zhang ◽  
Baoxun Yuan ◽  
Zhinan Zhang
Keyword(s):  
Firefly Algorithm ◽  
Fitness Function ◽  
Assembly Sequence Planning ◽  
Movement Direction ◽  
Assembly Planning ◽  
Optimal Size ◽  
Planning Problem ◽  
Assembly Sequence ◽  
Sequence Planning ◽  
Double Population

Assembly sequence planning is a critical step of assembly planning in product digital manufacturing. It is a combinational optimization problem with strong constraints. Many studies devoted to propose intelligent algorithms for efficiently finding a good assembly sequence to reduce the manufacturing time and cost. Considering the unfavorable effects of penalty function in the traditional algorithms, a new discrete firefly algorithm is proposed based on a double-population search mechanism for the assembly sequence planning problem. The mechanism can guarantee the population diversity and enhance the local and global search capabilities by using the parallel evolution of feasible and infeasible solutions. All parts composed of the assembly are assigned as the firefly positions, and the corresponding movement direction and distance of each firefly are defined using vector operations. Three common objectives, including assembly stability, assembly polymerization and change number of assembly direction, are taken into account in the fitness function. The proposed approach is successfully applied in a real-world assembly sequence planning case. The sizes of feasible and infeasible populations are adequately discussed and compared, of which the optimal size combination is used for initializing the firefly algorithm. The application results validate the feasibility and effectiveness of the discrete double-population firefly algorithm for solving assembly sequence planning problem.

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