A Methodology for Assembly Sequence Optimization by Hybrid Cuckoo-Search Genetic Algorithm

2018 ◽  
Vol 17 (01) ◽  
pp. 47-59 ◽  
Author(s):  
G. V. S. K. Karthik ◽  
Sankha Deb
Keyword(s):  
Genetic Algorithm ◽  
Cuckoo Search ◽  
Metaheuristic Algorithm ◽  
Assembly Sequence ◽  
Sequence Optimization ◽  
Assembly Sequences ◽  
Ga Algorithm ◽  
The Cost ◽  
The Given ◽  
Optimal Assembly

In this paper, we have proposed and implemented a methodology for assembly sequence optimization by using a nature-inspired metaheuristic algorithm, known as hybrid cuckoo-search genetic algorithm (CSGA). The cost criteria for optimization in the present formulation takes into consideration the total assembly time and the number of reorientations during the assembly process. To demonstrate the application of the CSGA, an example assembly containing 19 parts has been presented and the results have been compared with those of another metaheuristic algorithm, Genetic Algorithm (GA). From the results, it has been observed that for the given problem, the CSGA not only produces optimal assembly sequences with costs comparable to that of GA, but the convergence of CSGA algorithm has been found to be faster than the GA algorithm.

Determining Optimal Assembly Sequences for a Robotic Autonomous Assembly Task

2013 ◽  
Author(s):  
Bin Guo ◽  
Tao Zhang ◽  
Xinwei Wang ◽  
Ying Wang
Keyword(s):  
Genetic Algorithm ◽  
Assembly Sequence ◽  
Assembly Task ◽  
Assembly Sequences ◽  
Autonomous Assembly ◽  
Simulation Results ◽  
Crossover And Mutation ◽  
Optimal Assembly

This paper proposes a genetic algorithm to determine the optimal assembly sequence for a robotic autonomous assembly task. The coding rule of chromosomes, the selection, crossover and mutation operations are presented. In order to avoid invalid chromosomes, a modified crossover operation is suggested. In addition, a specific fitness table is created to evaluate a random assembly sequence. Some simulation results are employed to validate the effectiveness and accuracy of the proposed approach.

A Genetic Algorithm for Solving an Assembly Sequence Problem

2017 ◽  
Vol 872 ◽  
pp. 420-424
Author(s):  
Fawaz Saad Alharbi ◽  
Qian Wang
Keyword(s):  
Genetic Algorithm ◽  
Cost Effective ◽  
Design Stage ◽  
Manufacturing Companies ◽  
Assembly Sequence ◽  
Early Design ◽  
Early Design Stage ◽  
Heuristic Approaches ◽  
Assembly Sequences ◽  
Optimal Assembly

If a product has more than one component, then it must be assembled. The complexity of assembling a product is often subject to the number of assembly components that lead to possible assembly sequences in various forms. Also, it is widely understood that efficiency of assembling a product by reducing assembly times (therefore costs) is vital particularly for small manufacturing companies to survive in an increasingly competitive market. Ideally, it is helpful for determining an optimal assembly sequence of a product at the early design stage. Nevertheless, it may find inefficient using the heuristic approaches in acquisition of a quick solution in terms of an optimal assembly sequence with a minimal assembly time. By contrast, the study indicates that the genetic algorithm (GA) can be used as a cost-effective way for solving an assembly sequence optimisation problem of a product. This paper presents an investigation into a GA used for solving an assembly sequence problem of a ball pen. The study demonstrates that it can provide a quick solution in obtaining an optimal or near-optimal assembly sequence of the product for a small-sized company.

Petri Net Models for Assembly Sequences Planning

2007 ◽  
Vol 10-12 ◽  
pp. 411-415 ◽  
Author(s):  
Y.L. Fu ◽  
R. Li ◽  
H.B. Feng ◽  
Y.L. Ma
Keyword(s):  
Petri Net ◽  
Assembly Sequence Planning ◽  
Assembly Model ◽  
Assembly Sequence ◽  
Assembly Operation ◽  
Knowledge Based ◽  
Assembly Sequences ◽  
Model Size ◽  
Assembly Sequences Planning ◽  
Optimal Assembly

Assembly sequences can be represented by a Petri net(PN) which characterizes dynamic system changes and provides a tool for obtaining optimal assembly sequences. In this study some assembly operation constraints are considered in order to obtain more practical sequences which are conformed to real situations. In order to enhancing the efficiency of the assembly sequence planning, knowledge-based Petri net, combining an usual Petri net with expert’s knowledge and experiences, is proposed to construct the assembly model. With the complexity of the product, the product’s assembly model size will be too large to analysis. So the basic subnets are used to reduce the large PN. And the reduced version can be used for the analysis of the original PN. To verify the validity and efficiency of the approach, a variety of assemblies including some complicated products from industry are tested, and the corresponding results are also presented.

Influence of assembly predicate consideration on optimal assembly sequence generation

2015 ◽  
Vol 35 (4) ◽  
pp. 309-316 ◽  
Author(s):  
M. V. A. Raju Bahubalendruni ◽  
Bibhuti Bhusan Biswal ◽  
Manish Kumar ◽  
Radharani Nayak
Keyword(s):  
Low Cost ◽  
Problem Formulation ◽  
Search Space ◽  
Computational Time ◽  
Assembly Sequence ◽  
Content Type ◽  
Sequence Generation ◽  
Assembly Sequences ◽  
Assembly Sequence Generation ◽  
Optimal Assembly

Purpose – The purpose of this paper is to find out the significant influence of assembly predicate consideration on optimal assembly sequence generation (ASG) in terms of search space, computational time and possibility of resulting practically not feasible assembly sequences. An appropriate assembly sequence results in minimal lead time and low cost of assembly. ASG is a complex combinatorial optimisation problem which deals with several assembly predicates to result an optimal assembly sequence. The consideration of each assembly predicate highly influences the search space and thereby computational time to achieve valid assembly sequence. Often, the ignoring an assembly predicate leads to inappropriate assembly sequence, which may not be physically possible, sometimes predicate assumption drastic ally raises the search space with high computational time. Design/methodology/approach – The influence of assuming and considering different assembly predicates on optimal assembly sequence generation have been clearly illustrated with examples using part concatenation method. Findings – The presence of physical attachments and type of assembly liaisons decide the consideration of assembly predicate to reduce the complexity of the problem formulation and overall computational time. Originality/value – Most of the times, assembly predicates are ignored to reduce the computational time without considering their impact on the assembly sequence problem irrespective of assembly attributes. The current research proposes direction towards predicate considerations based on the assembly configurations for effective and efficient ASG.

scholarly journals Robotic Optimal Assembly Sequence Using Improved Cuckoo Search Algorithm

2018 ◽  
Vol 133 ◽  
pp. 323-330 ◽  
Author(s):  
Gunji Bala Murali ◽  
Bbvl Deepak ◽  
Bb Biswal ◽  
Golak Bihari Mohanta ◽  
Amruta Rout
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Assembly Sequence ◽  
Improved Cuckoo Search Algorithm ◽  
Optimal Assembly

An intelligent approach towards optimal assembly sequence generation

2016 ◽  
Vol 232 (4) ◽  
pp. 531-541 ◽  
Author(s):  
MVA Raju Bahubalendruni ◽  
Bibhuti Bhusan Biswal
Keyword(s):  
Objective Function ◽  
Efficient Method ◽  
Assembly Sequence ◽  
Sequence Generation ◽  
Computational Performance ◽  
Assembly Sequences ◽  
Working Principle ◽  
Assembly Sequence Generation ◽  
Intelligent Approach ◽  
Optimal Assembly

In this paper, a novel and efficient method is developed and proposed to obtain all valid assembly sequences and optimized assembly sequence for a given assembled product. The working principle of methodology is clearly illustrated with different example products. Four basic predicates namely “liaison predicate, geometrical feasibility, mechanical feasibility, and stability” are considered to validate each sequence. The proposed method is effective and proven efficient in the resulting all set of feasible assembly sequences described. Tool/gripper changes, assembly orientation changes, and part trajectory distances are considered to state the optimality of a valid assembly sequence. Achieving optimized assembly sequence through the proposed method for user-defined objective function is briefly illustrated. The computational performance of the method in achieving all valid assembly sequences is illustrated for various dissimilar products.

A Particle Swarm Algorithm for Assembly Sequence Planning

2010 ◽  
Vol 97-101 ◽  
pp. 3243-3246
Author(s):  
Yan Feng Xing ◽  
Yan Song Wang ◽  
Xiao Yu Zhao
Keyword(s):  
Particle Swarm ◽  
Tolerance Analysis ◽  
Assembly Sequence Planning ◽  
Particle Swarm Algorithm ◽  
Assembly Sequence ◽  
Sequence Generation ◽  
Assembly Sequences ◽  
Swarm Algorithm ◽  
Assembly Sequence Generation ◽  
Optimal Assembly

A particle swarm algorithm is proposed to generate optimal assembly sequences for compliant assemblies. Firstly, the liaison graph and the adjacency matrix describe the geometry of the compliant assemblies. An assembly sequence is represented by a character string, whose length is the number of all parts. The conceptual tolerance analysis is used to evaluate feasible sequences. Thereafter, the particle swarm algorithm is presented to generate assembly sequences, in which the elite ratio is applied to improve optimization results. Finally a fender assembly is used to illustrate the algorithm of assembly sequence generation and optimization.

Research on Assembly Optimization of Hull Section Based on the Genetic Algorithms

2013 ◽  
Vol 397-400 ◽  
pp. 1073-1077
Author(s):  
Wei Jun Xu ◽  
Long Kan Wang ◽  
Zhi Fan Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Virtual Assembly ◽  
Assembly Sequence ◽  
Section Model ◽  
Assembly Technology ◽  
Reducing Costs ◽  
Assembly Procedure ◽  
Optimal Assembly

This paper is concerned with the study on the assembly optimization of a hull section based on the genetic algorithms. It is significant important for improving efficiency of shipbuilding. Firstly, a typical hull section is selected as the analysis target for the assembly optimization. Then, the period and cost of shipbuilding is combined with the genetic algorithms, in which, the optimal assembly sequence is very important for the analysis, and it should be drown up. Finally, the hull section model is constructed by using CATIA software, and the simulation demo of the assembly procedure is carried out. The Genetic algorithm is a global optimizing method which can improve the calculation speed by using the subassembly. The virtual assembly of hull section based on the genetic algorithm is carried out under the environment of CATIA software, which including entity design, assembling and post-processing analysis and so on. The virtual assembly technology can be widely combined with the engineering production, not only have a significant effect on reducing costs and shortening the period of production, but also improve the quality of production. It is very useful for providing valuable reference in the actual productions.

Assembly Process Planning Method for Complex Electromechanical Products

2014 ◽  
Vol 488-489 ◽  
pp. 1260-1263
Author(s):  
Bo Sun ◽  
Lu Liu
Keyword(s):  
Genetic Algorithm ◽  
Process Planning ◽  
Ant Colony Algorithm ◽  
Planning Method ◽  
Assembly Process ◽  
Assembly Sequence ◽  
Constraint Matrix ◽  
Assembly Sequences ◽  
Electromechanical Products ◽  
Novel Method

Currently, neither the efficiency nor the effectiveness is sufficient in the area of the assemble optimization that commonly involves the genetic algorithm. A novel method to solve the cumbersome problem in the optimization of assembly sequences was proposed. On the basis of the assembly constraint matrix, the optimized assembly sequence is obtained with the proposed evaluating factors of the process requirement. That is the evolution of the original genetic algorithm to a certain extent. The effectiveness of the proposed method was proved by the comparison with the ant colony algorithm.

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