利用数据农业和机器学习减少响应时间

Falk Stefan Pappert, O. Rose
{"title":"利用数据农业和机器学习减少响应时间","authors":"Falk Stefan Pappert, O. Rose","doi":"10.11128/sne.31.tn.10567","DOIUrl":null,"url":null,"abstract":". In industry, there are numerous applications for simulation. However, simulation in our area usually takes some time even if a preexisting model just needs to be parameterized; there is still the run time, which will usually take at least a few minutes if not hours. In our current case, a planner wanted to know for a given product mix situation and for an equipment group with specific characteristics how much he can utilize the equipment without violating flow factor targets. A question, which arises several times during a typical workday as new or-ders are coming in and the situation on the shop floor is continuously changing. Since the user is usually asking the same question just with different parameters we are able to solve the waiting time problem while still giving good decision support. Instead of simulating every scenario at the time the user actually needs these answers, we use data farming to generate a large set of data points that are then used to train a neural network. This neural network then substitutes for the simulation and responds to the user immediately.","PeriodicalId":262785,"journal":{"name":"Simul. Notes Eur.","volume":"144 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Reducing Response Time with Data Farming and Machine Learning\",\"authors\":\"Falk Stefan Pappert, O. Rose\",\"doi\":\"10.11128/sne.31.tn.10567\",\"DOIUrl\":null,\"url\":null,\"abstract\":\". In industry, there are numerous applications for simulation. However, simulation in our area usually takes some time even if a preexisting model just needs to be parameterized; there is still the run time, which will usually take at least a few minutes if not hours. In our current case, a planner wanted to know for a given product mix situation and for an equipment group with specific characteristics how much he can utilize the equipment without violating flow factor targets. A question, which arises several times during a typical workday as new or-ders are coming in and the situation on the shop floor is continuously changing. Since the user is usually asking the same question just with different parameters we are able to solve the waiting time problem while still giving good decision support. Instead of simulating every scenario at the time the user actually needs these answers, we use data farming to generate a large set of data points that are then used to train a neural network. This neural network then substitutes for the simulation and responds to the user immediately.\",\"PeriodicalId\":262785,\"journal\":{\"name\":\"Simul. Notes Eur.\",\"volume\":\"144 3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Simul. Notes Eur.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.11128/sne.31.tn.10567\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Simul. Notes Eur.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11128/sne.31.tn.10567","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

. 在工业中,仿真有许多应用。然而,在我们的领域中,即使预先存在的模型只需要参数化,模拟通常也需要一些时间;仍然存在运行时间问题,这通常至少需要几分钟,如果不是几个小时的话。在我们当前的案例中,规划人员想知道给定的产品组合情况和具有特定特性的设备组,他可以在不违反流量因子目标的情况下利用多少设备。在一个典型的工作日里,随着新订单的到来和车间的情况不断变化,这个问题会出现好几次。由于用户通常会用不同的参数询问相同的问题,所以我们能够在提供良好决策支持的同时解决等待时间问题。我们不是在用户真正需要这些答案的时候模拟每个场景,而是使用数据农场来生成大量数据点,然后用于训练神经网络。然后,这个神经网络代替模拟并立即响应用户。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reducing Response Time with Data Farming and Machine Learning
. In industry, there are numerous applications for simulation. However, simulation in our area usually takes some time even if a preexisting model just needs to be parameterized; there is still the run time, which will usually take at least a few minutes if not hours. In our current case, a planner wanted to know for a given product mix situation and for an equipment group with specific characteristics how much he can utilize the equipment without violating flow factor targets. A question, which arises several times during a typical workday as new or-ders are coming in and the situation on the shop floor is continuously changing. Since the user is usually asking the same question just with different parameters we are able to solve the waiting time problem while still giving good decision support. Instead of simulating every scenario at the time the user actually needs these answers, we use data farming to generate a large set of data points that are then used to train a neural network. This neural network then substitutes for the simulation and responds to the user immediately.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信