基于改进粒子群算法的多机器多任务3D打印智能调度方法
2022,30(8):245-250
摘要：针对批量3D打印成本高,多机器多任务的3D打印批次调度复杂的问题,建立以最小单位体积平均成本为目标的优化模型,并提出一种基于改进粒子群算法的智能调度方法求解该模型；首先,分析打印工场、生产流程,构建3D打印单位体积平均成本模型；之后基于改进粒子群算法,以单位体积平均成本为适应度,以调度序列为粒子的位置信息,采用十进制顺序二维编码方式表示问题的解,并在更新策略上应用线性递减权值的动态惯性因子来调整全局与局部的搜索能力；算法迭代后,得到目标函数最优值及对应解集；经实验算例结果表明,该方法较单独打印加工的单位体积平均成本降低了0.101 3GBP/cm3,有效地降低工厂生产的总成本,提高了3D打印机的利用效率。
关键词：3D打印;粒子群算法;批次调度;单位体积平均成本;多机器多任务
Multi-task and Multi-machine 3D Printing Intelligent Scheduling Method with Particle Swarm Optimization Algorithm
Abstract：Aiming at the problems of high cost of batch 3D printing and complex batch scheduling of multi-task and multi-machine 3D printing, an optimization model aiming at minimum average cost per unit volume was established, and an intelligent scheduling method with improved particle swarm optimization algorithm was proposed to solve the model. Firstly, the average cost per unit volume of 3D printing was built by analyzing the printing workshop and production process. Then, with the improved particle swarm optimization algorithm, the average cost per unit volume was taken as the fitness, and the scheduling sequence was taken as the location information of the particles. The solution of the problem was represented by two-dimensional coding in order, and the dynamic inertia factor of linear decreasing weight was applied to adjust the global and local searching ability. After the algorithm is iterated, the optimal value of the objective function and the corresponding solution set are obtained. The experimental results show that the average cost per unit volume of 3D printing can be reduced by 0.101 3GBP/cm3 compared with that of single printing process, and the total production cost can be effectively reduced and the utilization efficiency of 3D printer can be improved.
Key words：3D printing; Particle swarm optimization; Batch scheduling; Average cost per unit volume; Multi-task and multi-machine
收稿日期：2022-03-31
基金项目：国家重点研发项目(2020YFB1711500)
