关键词: 混流装配线, 多目标排序, 多目标优化, 粒子群算法, Pareto排序, 模拟退火算法, 数学模型

Abstract: Aiming at the multi-objective sequencing problem in mixed model assembly lines, a mathematical model was proposed with the optimization objectives of minimizing total utility work, total production rate variation and total setup cost. Besides, an improved Multi-Objective Particle Swarm Optimization (MOPSO) was proposed to solve the model. In the algorithm, job-based coding was introduced and a new decoding method was put forward. Pareto ranking and niche count were employed to evaluate an individual, and a new fitness function was formed on these basis. In the update process of personal best, non-dominated particle and dominated particle were differentially updated so as to avoid lose of optimal solution. Furthermore, two strategies were adopted to overcome the drawback of premature convergence in particle swarm optimization： Simulated Annealing(SA) was introduced into the update of personal best and the selection of global best was extended to the whole swarm. Several numerical examples were presented to study the convergence, distribution and efficiency of the proposed algorithm, and the results showed the superiority of the algorithm.

Key words: mixed model assembly line, multi-objective sequencing, multi-objective optimization, particle swarm optimization, Pareto ranking, simulated annealing algorithm, mathematical models