Application of memetic algorithm in assembly sequence planning

Gao, Liang; Qian, Weirong; Li, Xinyu; Wang, Junfeng

doi:10.1007/s00170-009-2449-1

Cited by 55 publications

(23 citation statements)

References 18 publications

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“…The optimum set of parameters found to be able to provide the optimal/near optimal assembly sequence in reasonable time is population size of 10, fraction of worst eggs that are thrown out 10%, probability used in generating new eggs to replace those that are thrown out 90%. After the CS algorithm is run using the above set of parameters, the optimal assembly sequence obtained is [1,2,3,4,5,6,7,8,9,10,11,12,13,14] with fitness value of 1.5269, requiring 7 tool changes, 4 reorientation changes, stability index of 9, having the base component at the first location of the assembly sequence, and most importantly the sequence was found to have no feasibility (precedence) violations. In order to evaluate the effectiveness of the proposed CS algorithm, it is compared with Improved Harmony Search (IHS) algorithm [12].…”

Section: Results Of Proposed Cs Algorithm For Assembly Sequence Optimmentioning

confidence: 99%

“…In order to evaluate the effectiveness of the proposed CS algorithm, it is compared with Improved Harmony Search (IHS) algorithm [12]. The best sequence generated by the IHS algorithm is found to be [ 6,11,12,13,14,7,8,9,10] with fitness value of 1.5077, requiring 7 tool changes, 5 reorientation changes, stability index of 9, having base component at the first location of the assembly sequence, no feasibility violations but is clearly sub-optimal when compared to the sequence generated by CS algorithm. Figure 8 shows comparison between the convergence graphs of the CS and IHS algorithms from which it can be concluded that the proposed CS algorithm clearly outperforms IHS in terms of convergence speed as it is able to reach the global best fitness value in lesser number of iterations.…”

Section: Results Of Proposed Cs Algorithm For Assembly Sequence Optimmentioning

confidence: 99%

“…Lv and Lu [9] developed a discrete PSO considering tool and orientation changes, and operation type changes and interference times. Gao et al [10] presented an MA approach, with a chromosome representing an sequence consisting of genes containing part number and direction. They considered times of the direction changes.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Robotic Assembly Sequence Planning and Optimization by Cuckoo Search Algorithm

Mishra¹,

Deb²

2018

dtetr

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The assembly sequence optimization problem is challenging as number of sequences rises staggeringly with part count making it combinatorically explosive, tedious and time consuming to solve. A Computer-Aided Process Planning (CAPP) system for robotic assembly by a discrete cuckoo search algorithm has been proposed by us to generate feasible and optimal sequences considering minimization of orientation changes, tool/gripper changes, assembly stability and base component location. A case study and comparison results with an established soft computing approach are presented to show its effectiveness. It is further integrated with an upstream assembly product database to extract information on assembly directions, contact details, assembly precedence constraints and tool/grippers. Further the assembly sequence output from CAPP system is integrated with a robot task planner developed using Knowledge Based System capable of automatically generating executable robot level program for performing the assembly.

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Section: Results Of Proposed Cs Algorithm For Assembly Sequence Optimmentioning

confidence: 99%

Section: Results Of Proposed Cs Algorithm For Assembly Sequence Optimmentioning

confidence: 99%

Section: Introduction and Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Robotic Assembly Sequence Planning and Optimization by Cuckoo Search Algorithm

Mishra¹,

Deb²

2018

dtetr

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“…Shan et al (2009) used genetic simulated annealing (GSA) algorithm and ant colony optimization (ACO) algorithm to solve the ASPO problem. Gao et al (2010) made a comparison among geometric reasoning approach, knowledge-based approach and heuristic method, and found the optimal assembly sequence by a memetic algorithm. Wang and Liu (2010) proposed a chaotic particle swarm optimization approach to plan and optimize the assembly sequence of complex products.…”

Section: Introductionmentioning

confidence: 99%

Research on Assembly Sequence Planning and Optimization of Precast Concrete Buildings

Wang

Yuan

Sun

2018

Journal of Civil Engineering and Management

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Abstract. Due to more complex structure and increasing prefabrication rate of precast concrete buildings, the assembly order between their constituent components is getting more and more attention. In order to solve the assembly sequence planning and optimization (ASPO) problem in precast concrete buildings, Building Information Modelling (BIM) and Improved Genetic Algorithm (IGA) are organically combined to propose a new method called BIM-IGA-based ASPO method. This method uses BIM for parametric modelling, uses IGA to search for an optimal assembly sequence, and then uses BIM again for visual simulation to further test the assembly sequence. Besides, IGA, which is improved in coding mode, crossover operation and mutation operation, is also used to achieve the dynamic adjustment of assembly sequence in construction process. A full-text example is used to explain the detailed operating principle of BIM-IGA-based ASPO method. The results indicate that the method can effectively find an optimal assembly sequence to reduce the assembly difficulty of a precast concrete building.

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“…Recently, with the rapid development and wide application of digital and information technology [2,3], intelligent computing [4,5], virtual reality (VR) [6,7], and augmented reality (AR) technology [8], assembly planning can be efficiently carried out by these new methods. Many excellent assembly systems have been developed using the above techniques.…”

Section: Introductionmentioning

confidence: 99%

A computer vision-based assistant system for the assembly of narrow cabin products

Liu

Wang

et al. 2014

Int J Adv Manuf Technol

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Due to the narrowness of space and the complexity of structure, the assembly of cabin parts has become one of the major bottlenecks in the whole manufacturing process. This paper presents a computer vision-based assistant system that integrates assembly planning, assembly training and guidance, assembly status inspection, and assembly quality evaluation to improve the assembly efficiency and quality of cabin products. By using a novel real-time 3D object registration approach, the mixed reality technology is applied to assembly training and guidance of cabin parts. A machine vision-based custom-made inspection system is designed for assembly surplus detection and gap measurement in half-enclosed cabin space. A cabin assembly quality model is proposed on the basis of Markov chain to quantitatively evaluate the cabin assembly quality. A case study is given to demonstrate the practical application of the proposed system.

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Application of memetic algorithm in assembly sequence planning

Cited by 55 publications

References 18 publications

Robotic Assembly Sequence Planning and Optimization by Cuckoo Search Algorithm

Robotic Assembly Sequence Planning and Optimization by Cuckoo Search Algorithm

Research on Assembly Sequence Planning and Optimization of Precast Concrete Buildings

A computer vision-based assistant system for the assembly of narrow cabin products

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