关键词: 轴承, 多目标, 物料配送, 自动导引运输车, 遗传算法

Abstract: Aiming at the problem that traditional material distribution methods are difficult to meet the requirements of flexible intelligent manufacturing of bearings, an adaptive optimization method of material distribution intervals and quantity with multiple varieties and variable batch size is proposed. First, taking the distribution cost, the cost of inventory beside a production line and the number of automated guided vehicles as the optimization objectives, and taking the material quantity, the distribution capacity of automated guided vehicles and the distribution time as constraints, a multi-objective cooperative optimization model for distribution intervals and quantity of multi-frequency and small-batch materials is established. Then, according to the characteristics of the decision variables in the optimization model, using real values that reflect distribution information for coding, a fast non-dominated sorting genetic algorithm is designed with modified crowding calculation method and elitism strategy to improve the optimization ability of genetic algorithm. Finally, the proposed optimization method is verified with an application example. Results show that: compared to non-optimized scenarios, the average distribution batch size is reduced by 42%, the average distribution interval is shortened by 30%, and the total distribution cost can be reduced by more than 17% after optimization, which realizes the self-adaptation and self-decision of material distribution intervals and quantity under different bearing types, and effectively reduces the total distribution cost.

Key words: bearing, multi-objective, material distribution, automatic guided vehicle (AGV), genetic algorithm