Sequencing Mixed-model Production Systems by Modified Multi-objective Genetic Algorithms

As two independent problems, scheduling for parts fabrication line and sequencing for mixed-model assembly line have been addressed respectively by many researchers. However, these two problems should be considered simultaneously to improve the efficiency of the whole fabrication/assembly systems. By far, little research effort is devoted to sequencing problems for mixed-model fabrication/assembly systems. This paper is concerned about the sequencing problems in pull production systems which arc composed of one mixed-model assembly line with limited intermediate buffers and two flexible parts fabrication flow lines with identical parallel machines and limited intermediate buffers. Two objectives are considered simultaneously: minimizing the total variation in parts consumption in the assembly line and minimizing the total makespan cost in the fabrication/assembly system. The integrated optimization framework, mathematical models and the method to construct the complete schedules for the fabrication lines according to the production sequences for the first stage in fabrication lines are presented. Since the above problems are non-deterministic polynomial-hard(NP-hard), a modified multi-objective genetic algorithm is proposed for solving the models, in which a method to generate the production sequences for the fabrication lines from the production sequences for the assembly line and a method to generate the initial population are put forward, new selection, crossover and mutation operators are designed, and Pareto ranking method and sharing function method are employed to evaluate the individuals' fitness. The feasibility and efficiency of the multi-objective genetic algorithm is shown by computational comparison with a multi-objective simulated annealing algorithm. The sequencing problems for mixed-model production systems can be solved effectively by the proposed modified multi-objective genetic algorithm.