Digital Twin-driven Customized Individual Equipment Packing Production Optimization

doi:10.12382/bgxb.2024.0200

Abstract

Abstract:

Individual equipment is closely related to the user’ height and weight as well as the task division,and has the characteristics of high customization and mass production,which bring great challenges to the production and delivery of individual equipment.Packing is the last step of individual equipment production,and is also the most difficult step to achieve customization and mass production.To solve this problem,an optimization method of individual equipment packing production process based on digital twin is proposed.First,a digital twin model of individual equipment packing production process is established.Secondly,the whole production process of individual equipment,such as distribution and replenishment,transportation,workers’ picking and boxing,is described by simulation,and the production process is optimized by an improved genetic algorithm.Finally,the proposed method is actually applied in equipment production.The optimal design and optimization of the cargo location,the number of workers and the number of handling equipment are realized by iterative simulation,thus effectively solving the problems of highly dynamic,congestion and transportation conflicts,long production cycle,unbalanced busy and idle stations,and delayed detection of misloading in large-scale customized production of individual equipment.The production efficiency of soldier equipment packing is improved by 11.18%.

Key words: equipment selection, large-scale customized production, digital twin, improved genetic algorithm, production optimization

CLC Number:

TJ05

GAO Feng, ZHAO Ning, ZHUANG Cunbo, YU Dongmei. Digital Twin-driven Customized Individual Equipment Packing Production Optimization[J]. Acta Armamentarii, 2025, 46(4): 240200-.

Figures/Tables 24

Fig.1 Sketch map of packing flow production line

Fig.2 Production line optimization based on digital twin

Fig.3 Connection method of virtual and physical workshops

Table 1 Key parameters

类别	参数
上线录入信息	箱子绑定的订单号,箱子拣货的清单,箱子上线的时间
补货任务单	货位(商品),数量/箱,数量/零散
下线统计信息	订单拣选完成时间,完成订单的数量,目前所有订单的总完成时间,订单的平均完成时间

Fig.4 Packing production line replenishment simulation flowchart

Fig.5 Simulation flowchart of packing production line

Fig.6 Simulation optimization process

Fig.7 Encoding diagram of genetic algorithm

Fig.8 Flowchart of adaptive elite genetic algorithm

Fig.9 Location configuration table

Fig.10 Packing flowchart of individual equipment

Fig.11 Working simulation flowchart

Table 2 Verification cases

案例	装箱工位	AGV数量	叉车数量	工人数量
1	8	8	8	23
2	8	6	6	18
3	12	12	12	30
4	12	10	10	26
5	16	14	14	32
6	16	12	12	30

Table 3 Completion times before ans after cargo location optimization

案例	完工时间/min		优化率/%
案例	优化前	优化后	优化率/%
1	62.86	55.68	11.42
2	94.17	85.52	9.74
3	122.72	105.08	11.17
4	115.00	100.36	11.46
5	176.53	152.57	11.88
6	150.12	128.00	11.42

Table 4 Mean usage quantities of AGVs and forklifts before and after handing equipment optimization

案例	AGV使用数量		叉车使用数量
案例	优化前	优化后	优化前	优化后
1	8	3	8	2
2	6	2	6	2
3	12	7	12	6
4	10	6	10	6
5	14	10	14	11
6	12	10	12	8

Fig.12 Fitness trend chart of traditional genetic algorithm

Fig.13 Fitness trend chart of hybrid genetic algorithm

Fig.14 Fitness trend chart of adaptive elite genetic algorithm

Fig.15 Input interface for the number of workers and handing equipment

Fig.16 Flow racking distribution process

Fig.17 AGV handing goods

Fig.18 Worker handing goods

Fig.19 Order box selection process

Fig.20 Example of statistical chart

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