A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines

Sequencing problems are important for an efficient use of mixed model assembly lines. There is a rich set of criteria on which to judge sequences of product models in terms of line utilization. We consider three practically important objectives: minimizing total utility work, keeping a constant rate of part usage and minimizing total setup cost. A considerate line manager would like to take into account all these factors. The multiple objective sequencing problem is described and its mathematical formulation is provided. A genetic algorithm is designed for finding near-Pareto or Pareto optimal solutions for the problem. A new genetic evaluation and selection mechanism, called Pareto stratum–niche cubicle, is proposed. The performance comparison of the proposed genetic algorithm with three existing genetic algorithms is made for various test-bed problems in terms of solution quality and diversity. The results reveal that the proposed genetic algorithm outperforms the existing genetic algorithms, especially for problems that are large and involve great variation in setup cost.