Multi-objective Optimization of Material Collaborative Delivery for Mixed Model Automotive Assembly Process

To solving the real-time material delivery problems from the warehouse to workstations of the mixed model automotive assembly line in accordance with the production cycle. The material flow of automotive assembly, type of material and the production characteristic were analyzed in order to feed material timely & properly. The material real-time delivery system based on field bus has been developed. The material pull systems based on Kanban technology was developed in the environment of JIT production environment. The network architecture of the material monitor system based on internet of things was established. A multi-objective materials delivery model considering from optimization objectives, constraints and impact factors was proposed. The materials transportation costs, materials transportation time and materials storage on-line are considered as optimize objective function. The hybrid particle swarm algorithm was designed and the coding design, particle generation, fitness function, crossover operation, mutation operation of the algorithm was presented. The validity of the model and algorithm was verified by a case of material collaborative delivery problem for gearbox assembly. The results show that the hybrid particle swarm algorithm is feasible and superior to genetic algorithm.