Abstract:

Two optimization objectives, minimizing the variation in crucial parts consumption and the makespan, are simultaneously considered to study the sequencing problems in car engine mixedmodel assembly lines under limited intermediate buffer size constraints. The mathematical models are presented. Since the problem addressed is NPhard, a multiobjective genetic algorithm is proposed, in which hybrid crossover operators and a heuristic mutation method are adopted, and the Pareto ranking method and the sharing function method are employed to evaluate the individuals′ fitness. The optimization result of the multiobjective genetic algorithm is compared with that of a multiobjective annealing algorithm. The comparison result illustrates that the multiobjective genetic algorithm proposed outperforms the multiobjective annealing algorithm in respect of the solutions′ desirability and also the computation efficiency. The multiobjective genetic algorithm is an efficient algorithm for solving the sequencing problem in mixedmodel assembly lines under buffer size constraints.

Key words: sequencing, engine, mixedmodel assembly line, buffer, multiobjective genetic algorithm