Sequencing Mixed Model Assembly Lines Based on a Modified Particle Swarm Optimization Multi-objective Algorithm

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.