Application Of Multi-objective Optimization And Fuzzy Decision-making For Mixed-model Assembly Sequencing

Mixed-model assembly lines are increasingly accepted in industry to cope with the trend of diversification of customer demands, and mixed-model sequencing problems are of particular importance for the reduction in cost and improvement in productivity. Research on multi-objective optimization of mixed-model sequencing problems is mainly about how to find a set of Pareto solutions, but little attention has been paid to how to select the most satisfactory one of them. Therefore, a two-stage model including Optimization and Decision-making is proposed. In the first stage of multi-objective optimization, a set of Pareto solutions is obtained; in the second stage of fuzzy decision-making, the best solution is selected from the set of Pareto solutions.In terms of establishing model for multi-objective optimization, the common characteristics of the mixed-model assembly lines are analyzed in this paper. We consider three practically important objectives: minimizing total utility time and idle time, leveling parts usage and minimizing total setup cost, basing on which the model of multi-objective optimization in mixed-model assembly line is established.The non-dominated sorting genetic algorithm II (NSGA-II) uses a fast non-dominated sorting method, an elitist approach and a crowed-comparison mechanism, improving both quality and diversity of Pareto solutions. So in this paper, NSGA-II is used to solve the model of multi-objective optimization. The solutions of two groups of problems prove to be better than MOGA in comparison of set coverage, spacing and maximum spread.Resolving the objective weight is the key factor of the decision-making problems. Because of the complexity of the mixed-model assembly line, the triangular fuzzy numbers are used to describe the fuzzy weighs of objectives, and the combination fuzzy weights will be determined by an objective and subjective synthetic method. Then the fuzzy TOPSIS approach is used to select best one form Pareto solutions.Finally, the model and algorithms are implemented in the Manufacturing Execute System (MES) for automobile production, which proves is validity.