Dynamic material handling scheduling for mixed-model assembly lines based on line-integrated supermarkets

In order to solve the material handling scheduling problem for mixed-model assembly lines based on line-integrated supermarkets, this paper presents an extreme learning machine and knowledge base-based dynamic scheduling method. First, the dynamic material handling scheduling problem is described and a mathematical model is established. Using this model, the weight sum of the output of the assembly line and the number of logistics workers is maximized under the condition of variable product ratio and weights of scheduling criteria. Also the random failure of the equipment and the instability of the cycle time are considered. Then, an extreme learning machine and knowledge base-based dynamic scheduling method are constructed. Considering the defects of extreme learning machine, an elite opposition learning self-adaptive differential evolution-based extreme learning machine is proposed. Finally, the simulation results prove the feasibility and effectiveness of the proposed dynamic scheduling method in the dynamic scheduling process. © 2020, Jilin University Press. All right reserved.