A dynamic material handling scheduling method based on elite opposition learning self-adaptive differential evolution-based extreme learning machine (EOADE-ELM) and knowledge base (KB) for line-integrated supermarkets

Since the highly diversified consumer demands and changing market environment pose a great challenge for the automobile industry, this paper presents an extreme learning machine and knowledge base-based dynamic scheduling method to solve the dynamic scheduling problem of material handling for mixed-model assembly lines based on line-integrated supermarkets. First, the dynamic material handling scheduling problem is described and a mathematical model is established to maximize the weight sum of the throughput of the assembly line and the number of logistics workers under the condition of variable product ratio and weights of scheduling criteria, considering the random failure of the equipment and the instability of the cycle time. Subsequently, an extreme learning machine and knowledge base-based dynamic scheduling method is constructed, consisting of a knowledge base-based scheduling rule selection and an extreme learning machine (ELM)-based product mix matching method. Afterward, considering the defects of extreme learning machine, an elite opposition learning self-adaptive differential evolution-based extreme learning machine (EOADE-ELM) is proposed to optimize the parameters of ELM. Elite opposition-based learning and self-adaptive operators are employed to the EOADE-ELM to improve the performance. Finally, the simulation results prove the feasibility and effectiveness of the proposed dynamic scheduling method in the dynamic scheduling process.