Job Shop Rescheduling Under Recessive Disturbance Based on Digital Twin

doi:10.16182/j.issn1004731x.joss.21-0110

Abstract

Abstract:

A new shop rescheduling model driven by digital twin is proposed to solve the problems of disturbance cumulative rescheduling. A scheduling parameter updating method is proposed and a random probability distribution is used to describe the distribution of scheduling parameters to improve the accuracy of scheduling parameters. An implicit disturbance detection model is built based on Siamese Network using real-time data as input to realize the start time of rescheduling. The sample data for scheduling knowledge mining are extracted from the historical scheduling scenarios. Through the Pseudo-Siamese CNN, the mapping relationship between the Process state and machine state is obtained, which is applied to production online rescheduling. Simulation experiments show the feasibility of the proposed digital twin driven shop rescheduling model.

Key words: recessive disturbance, digital twin, siamese network, dispatching rule mining

CLC Number:

TP391.9

Dinghui Wu, Tongrui Zhang, Xiuli Zhang. Job Shop Rescheduling Under Recessive Disturbance Based on Digital Twin[J]. Journal of System Simulation, 2022, 34(3): 573-583.

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序号	特征描述
1	车间内空闲的机器数
2	车间内正在加工的机器数
3	车间内空闲的工件数
4	车间内正在加工的工件数
5	机器正在加工的工件编号
6	机器所要加工的工序数
7	机器还未加工的工序数
8	机器已经加工的工序数
9	机器加工时长的总和
10	机器空闲时间总和
11	工件正在被加工的机器编号
12	工件的工序数
13	工件还未加工的工序数
14	工件已经加工的工序数
15	工件加工时长的总和
16	工件空闲时间总和

序号	描述
1	工件编号
2	工件待加工工序的编号
3	工件还未加工的工序数
4	工件已经加工的工序数
5	工件加工时长的总和
6	工件空闲时间总和

序号	描述
1	机器编号
2	机器前一个加工工件编号
3	机器已经加工的工序数
4	机器加工时长的总和
5	机器空闲时间总和
6	待加工工序在机器上的预计加工时间

方案类型	最大完工时间	完工时间偏差
扰动后调度方案	57.99	4.99
伪孪生网络方案	56.86	3.86
SPT调度方案	68.90	15.90

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