Application of Multi-Objective Culture Particle Swarm Optimization in Complex Product Assembly Line Balancing

Chang Yi Liu; Hai Jun  Wen

doi:10.4028/www.scientific.net/AMR.694-697.3526

Paper Titles

Study on the Product Quality Control in Small Batch Trial Process
p.3507

Forecasting and Analysis the Demand of Agricultural Mechanization for Economic Development
p.3512

Optimization Design for Size of Footwear Outer Packaging Boxes
p.3516

SNA Based Expert Knowledge Map Design for Ship-Block Scheduling Decision-Making
p.3522

Application of Multi-Objective Culture Particle Swarm Optimization in Complex Product Assembly Line Balancing
p.3526

Study on Cluster Model of Appliance Industry in Qingdao
p.3531

The Dynamic Adjustment of China's Manufacturing Listed EnterprisesDebt Ratio - A Study Based on System GMM Estimation
p.3535

A Quantitative Model for Safety Evaluation of Food Supply Chain Based on CAS
p.3540

Analysis of Development of Supply Chain in New Materials Industry
p.3545

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 694-697Application of Multi-Objective Culture Particle...

Application of Multi-Objective Culture Particle Swarm Optimization in Complex Product Assembly Line Balancing

Abstract:

As for the typeIassembly line balancing problem in complex products, the mathematical model of optimization goal is established with its optimization goal of minimizing the number of workstations as well as minimizing the differences in assembly complex relationship, and with the introduction of the framework of cultural evolution, a multi-cultural particle swarm algorithm is presented. The algorithm used arranged code so that the particle can still be able to meet the job constraints after its been decoded; using crowded distance to sort operator and remove the extra particles, in order to ensure a uniform distribution of the Pareto front; in order to improve the efficiency of the convergence of the algorithm, we adjust the flight parameters of particle basing on the dynamic changes in crowding distance. Through the comparison of standard test problems with other algorithms, we indicate the effectiveness of the proposed algorithm.

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Periodical:

Advanced Materials Research (Volumes 694-697)

Pages:

3526-3530

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.694-697.3526

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Online since:

May 2013

Authors:

Chang Yi Liu, Hai Jun Wen

Keywords:

Assembly Line Balancing, Cultural Algorithm, Manufacturing Complexity, Multi-Objective, Particle Swarm Optimization (PSO)

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