Mixed-model assembly line sequencing with hybrid genetic algorithm and simulation

With the more variable demand marketing, mixed-model assembly lines are paid more attention in many industries such as automotive manufacturing industry. Sequencing problems are important for an efficient use of mixed-model assembly lines. Traditionally, the focus of the sequencing problems is the optimization algorithm study. In the paper, the optimization algorithm and simulation tools are combined to provide more friendly environment for decision makers. The important objectives are optimized with the optimization algorithm. Then the results of the optimization are input to the simulation models to observe,the different effects to the minor objectives of different optimized results. A hybrid genetic-tabu search algorithm for multi-objective optimization of mixed-model assembly line sequencing is developed. The procedure and its advantages of the optimization plus simulation approach are showed with an experiment.