Abstract: Intelligent manufacturing digital workshop has the characteristics of high integration of Automated Guided Vehicle (AGV) and processing equipment.For the traditional way of intelligent manufacturing digital workshop layout and AGV path planning optimized independently,which made the materials over-concentrated in a certain area,and leaded to heavy workload of AGV in the region for affecting the result of AGV path planning,an integrated optimization model of intelligent manufacturing digital workshop layout and AGV path planning aimed at minimizing logistics transportation amounts,material transport time and number of AGV was proposed.To solve the model,an improved fast elitist Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) was designed.To make the initial population distribution more extensive and to avoid falling into local optimum,the system layout planning was used to generate some initial population.To improve the optimization speed and accuracy of algorithm,the unequal double-link chromosome and vaccination strategy were introduced.Afterwards,a set of Pareto solution sets was obtained,which could be optimized by decision-makers.The effective of the integration model was validated by the case study.

Key words: intelligent manufacturing, workshop layout, automated guided vehicle path planning, improved fast elitist non-dominated sorting genetic algorithm, vaccination strategy

摘要： 智能制造数字化车间具有自动导引车(AGV)、加工设备高度集成的特点。针对智能制造数字化车间布局和AGV路径规划分开优化,使得某区域的物料搬运过于集中,导致该区域AGV搬运工作量繁重,影响AGV路径规划的问题,建立了以物流运输量最小,物料搬运时长最短以及AGV数量最少为目标的智能制造数字化车间布局和AGV路径规划集成优化模型。提出一种改进的带精英策略的快速非支配排序遗传算法,采用系统化布置设计法来生成部分初始种群,增加初始种群分布,防止陷入局部最优;将不等长的双链染色体与疫苗接种策略引入非支配排序遗传算法,提高了算法的寻优速度和精度;最后求解得到一组Pareto解集,供决策者优中选优。通过实例验证了所提方法的有效性。

关键词: 智能制造, 车间布局, 自动导引车路径规划, 改进的带精英策略的快速非支配排序遗传算法, 疫苗接种策略