Simulation Platform for Optimization and Decision Making of Flexible Manufacturing Process of Automatic Production Lines

doi:10.16182/j.issn1004731x.joss.22-0077

Abstract

Abstract:

In order to simulate a flexible manufacturing process to verify the correctness of theoretical computation and analysis and find other important factors for optimization and decision making in the manufacturing process, a simulation platform for the optimization and decision making of the flexible manufacturing process of automatic production lines is designed and developed. The framework of the simulation platform is constructed with four hierarchies, including the enterprise production hierarchy, optimization and decision making hierarchy, data resource hierarchy, and function module hierarchy. On this basis, the functional implementation of the modules of production allocation, operation setting, and operation simulation in the function module hierarchy, as well as the support of the optimization and decision making module for the connection with internal-external algorithms and static-dynamic production line studies are demonstrated in detail. The developed platform is applied to simulate the manufacturing process of key components of cars in a high-end equipment manufacturing enterprise in Changzhou, Jiangsu, and the application process of the platform is illustrated.

Key words: automatic production line, flexible manufacturing process, operation simulation, three-dimensional roaming, simulation verification of theories

CLC Number:

TP391.9

Chao Fu, Dongyue Wang, Xinxin Li, Min Xue. Simulation Platform for Optimization and Decision Making of Flexible Manufacturing Process of Automatic Production Lines[J]. Journal of System Simulation, 2023, 35(5): 920-935.

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研究问题	求解算法	研究目标
单目标柔性作业调度^[14-16]	遗传算法，启发式方法，蚁群算法，免疫算法，模拟退火算法	最大完工时间最小，总完工时间最小，设备空闲时间最小，提前/拖期惩罚最小
多目标柔性作业车间调度^[17-19]	禁忌搜索算法，遗传算法，粒子群优化算法，模拟退火算法	生产成本最小，设备利用率最大，交货满意度最大，人工分配最优，完成时间最小
机器和AGV联合调度优化^[3,20-21]	启发式规则调度方法，离散粒子群算法	最大完工时间最小，AGV数量配置最优
不确定柔性作业调度^[22-24]	规则调度/神经网络，鲁棒自适应调度，遗传算法，基于模糊逻辑的改进遗传算法	生产响应速度最快，重调度最优，加工顺序最优，完成时间最小
AGV碰撞和拥堵^[25]	动态平衡策略，模糊神经网络，强化学习	加工路径最优，完工时间最小，AGV数量配置最优

零件	加工工序	M₁	M₂	M₃	M₄	M₅
ZJT01	车涡端	10	9999	10	9999	10
	车压端	6	6	6	9999	9999
	铣进出油孔面，钻孔攻丝	9999	8	9999	8	8
ZJT02	车涡端	7	7	7	9999	7
	车压端	4	4	4	9999	9999
	铣面、钻孔攻丝	6	9 999	9999	6	6
	铣进出水孔面，钻孔攻丝	9999	10	10	9999	9999
ZJT03	车涡端	3	3	9999	3	9999
	车压端	5	5	5	9999	9999
	铣进出油孔面，钻孔攻丝	9999	9999	9999	9	9
ZJT04	车涡端	9999	2	2	9999	2
	车压端	3	3	3	9999	9999
	铣面、钻孔攻丝	9999	9999	9999	8	8
	斜油孔	9	9999	9	9	9999
	铣进出水孔面，钻孔攻丝	9999	7	9999	7	7

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