Title :
Sequencing Mixed Model Assembly Lines Based on a Modified Particle Swarm Optimization Multi-objective Algorithm
Author :
Dong, Qiaoying ; Kan, Shulin ; Qin, Ling ; Huang, Zhihui
Author_Institution :
Shanghai Univ., Shanghai
Abstract :
Mixed model assembly lines are attractive means of mass and large-scale series production. Determination of the production sequence for different models is a key issue in the mixed model assembly line. Particle swarm optimization (PSO) is a novel metaheuristic inspired by the flocking behaviour of birds which has be used in consecutive problems successfully. However, it´s applications in the mixed model assembly line sequencing are extremely few. This paper attempts to use a modified particle swarm optimization algorithm to solve the mixed model assembly line sequencing problem in discrete space with two objectives: the total setup cost and total idle-overload cost. Compared with the original PSO, we modified the particle position representation and adapted it to the discrete code, and introduced a self-adaptive escape scheme to enhance the diversity of particles. A comparison between the basic PSO and our modified PSO show that our modified PSO algorithm is an effective sequencing method for mixed model assembly lines which possesses rich diversity.
Keywords :
assembling; costing; particle swarm optimisation; discrete code; metaheuristic; mixed model assembly lines sequencing; modified particle swarm optimization; multiobjective algorithm; particle position representation; production sequence; self-adaptive escape scheme; total idle-overload cost; total setup cost; Ant colony optimization; Assembly; Automation; Belts; Birds; Cost function; Educational institutions; Flow production systems; Mass production; Particle swarm optimization; mixed model assembly line; modified PSO; muti-objective; sequencing;
Conference_Titel :
Automation and Logistics, 2007 IEEE International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-1531-1
DOI :
10.1109/ICAL.2007.4339061