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.

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.

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

Stable Assembly Sequence Planning Using a Genetic Algorithm

1999 ◽  
Author(s):  
Shiang-Fong Chen ◽  
Xiao-Yun Liao
Keyword(s):  
Genetic Algorithm ◽  
Simple Structure ◽  
Fitness Function ◽  
Assembly Sequence Planning ◽  
Connection Matrix ◽  
Assembly Sequence ◽  
Sequence Planning ◽  
Industry Standard ◽  
Planning Algorithm ◽  
The Stability

Abstract Stability problems in assembly sequence planning have drawn great research interest in recent years. Most proposed methodologies are based on graph theory and involve complex geometric and physical analyses. As a result, even for a simple structure, it is difficult to take all the criteria into account and to implement real world solutions. This paper uses a genetic algorithm (GA) to synthesize different criteria fo generating a stable assembl plan. Three matrices (Connection Matrix, Supporting Matrix, and Interference-Free Matrix) are generated from an input B-rep file to represent the CAD information of a given product. The stability of a given assembly plan and reorientation numbers are incorporated into the fitness function of the genetic assembly planner. The proposed planning algorithm has been successfull implemented. This paper also presents implemented planne performance as measured for two industry-standard structures.

Automated Microassembly Sequence Planning With Sub-Assemblies

2016 ◽  
Author(s):  
Joseph Seymour ◽  
David J. Cappelleri
Keyword(s):  
Fitness Function ◽  
Engineering Problem ◽  
Assembly Sequence Planning ◽  
Human Beings ◽  
Assembly Sequence ◽  
Test Bed ◽  
Sequence Planning ◽  
Macro Scale ◽  
Limited Degree ◽  
Feasible Solutions

Assembly sequence planning is an engineering problem that has been of great interest in the manufacturing field at the macro-scale. As more complex assemblies are desired at the micro and nano scales it is no longer feasible for human beings to plan and execute the production of these systems. A promising algorithm that allows optimization of assembly sequence plans that has been developed is called the Breakout Local Search. One drawback of this algorithm is its inability to consider the need for intermediate sub-assemblies to generate feasible solutions. Here an expansion to the BLS algorithm, called the Sub Assembly Generating BLS (SABLS) algorithm, is proposed. The fitness function of this new algorithm is also tailored to the specific constraints and motion primitives for a micromanipulation test-bed allowing for its use in microassembly applications. It is shown that the proposed algorithm is capable of generating optimized solutions that can be assembled with this limited degree of freedom system.

Beyond Optimal Sequencing: Defining Part Orientation and Worker Allocation in Assembly

2015 ◽  
Author(s):  
Nima Rafibakhsh ◽  
Matthew I. Campbell
Keyword(s):  
Assembly Sequence Planning ◽  
Assembly Planning ◽  
Assembly Sequence ◽  
Sequence Planning ◽  
Worker Allocation ◽  
Optimal Sequencing ◽  
Novel Approach ◽  
Processing Step ◽  
Minimum Number ◽  
Part Orientation

Assembly Sequence planning is a tedious but crucial task in manufacturing a product. A good assembly plan will lead to minimum wasted time and maximum capacity of resources. Typically, research in Automated Assembly Planning and Assembly Sequence Planning (AAP and ASP) only define the sequence that the parts should be assembled with no information for specifying additional details to make the plan complete and optimal. In this paper we introduce a post-processing step (after the sequence of parts has been found) with focus on optimal part orientation and worker allocation. The paper has two main sections: the first section uses Dijkstra’s algorithm to obtain part orientation with minimum assembly cost. For the second part of the paper, a novel approach is proposed based on a line balancing technique to find the minimum number of workers needed to achieve the minimum make-span time. These necessary details in AAP give real time feedback to designers to analyze their design with production and assembly line information.

A hierarchical parallel multi-station assembly sequence planning method based on GA-DFLA

2020 ◽  
pp. 095440622097406
Author(s):  
Bo Wu ◽  
Peihang Lu ◽  
Jie Lu ◽  
Jinli Xu ◽  
Xiaogang Liu
Keyword(s):  
Hierarchical Structure ◽  
Assembly Line ◽  
Fitness Function ◽  
Information Matrix ◽  
Assembly Sequence Planning ◽  
Planning Method ◽  
Assembly Sequence ◽  
Cost Index ◽  
Sequence Planning ◽  
Structure Tree

In recent years, the parallel assembly sequence planning (PASP) is put forward to accommodate the development of greater variety in the mass-customization production. Although many researchers have made contributions to assembly sequence planning (ASP), how to construct parallel multi-station ASP model to effectively solve the integrated ASP and assembly line balancing (ALB) problem need to be further investigated. In this paper, a hierarchical parallel multi-station assembly sequence planning method based on the genetic algorithm and discrete frog leaping algorithm (GA-DFLA) is proposed to solve the integrated parallel multi-station ASP and ALB problem. The assembly information datasets of the model based definition (MBD) including parts information, hierarchical information, matrix information and resource information are defined firstly. Then the hierarchical structure tree is constructed according to the divided assembly units. The hierarchical parallel multi-station assembly sequence planning is carried out from bottom to top in hierarchical structure tree model using the GA-DFLA. The fitness function with feasibility index, time cost index and assembly line balance index, is proposed to determine a more appropriate solution. Finally, the rear independent suspension is taken as an example to validate this method. The results show that the time of parallel multi-station model is at least 35.27% less than the time of serial multi-station model.

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.

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