Stochastic Scheduling for Batch Processes With Downstream Queue Time Constraints

IF 2.3 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-09-20 DOI:10.1109/TSM.2023.3317679
Wen-Chi Chien;Ywh-Leh Chou;Cheng-Hung Wu
{"title":"Stochastic Scheduling for Batch Processes With Downstream Queue Time Constraints","authors":"Wen-Chi Chien;Ywh-Leh Chou;Cheng-Hung Wu","doi":"10.1109/TSM.2023.3317679","DOIUrl":null,"url":null,"abstract":"This research studies the problems of stochastic dynamic scheduling in production systems with batch processes and process queue time (PQT) constraints. The production systems consist of upstream batch processing machines and downstream single processing machines. Under the PQT constraint, waiting time in the downstream queue is constrained by an upper limit and violating this constraint causes scraps of jobs. The batch process increases the probability of PQT constraint violation because a batch of work-in-processes (WIPs) will move simultaneously into the downstream queue after the service completion of batch processes and suffer from higher waiting time variance. A batch process admission control (BPAC) model is developed using Markov decision processes to minimize the sum of long-run average waiting and scrap costs. The proposed BPAC model explicitly considers uncertain factors in production systems given that uncertainties are major reasons for PQT constraint violation. These uncertain factors include job arrival, processing time, and machine breakdown/repair. To cope with these uncertain factors, the BPAC control decisions change dynamically with the real-time machine health and WIP distribution. The performance of BPAC is validated using discrete event simulation, and the simulation results confirm the significant performance improvement in a wide range of batch production environments.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"36 4","pages":"599-610"},"PeriodicalIF":2.3000,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Semiconductor Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10256065/","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

This research studies the problems of stochastic dynamic scheduling in production systems with batch processes and process queue time (PQT) constraints. The production systems consist of upstream batch processing machines and downstream single processing machines. Under the PQT constraint, waiting time in the downstream queue is constrained by an upper limit and violating this constraint causes scraps of jobs. The batch process increases the probability of PQT constraint violation because a batch of work-in-processes (WIPs) will move simultaneously into the downstream queue after the service completion of batch processes and suffer from higher waiting time variance. A batch process admission control (BPAC) model is developed using Markov decision processes to minimize the sum of long-run average waiting and scrap costs. The proposed BPAC model explicitly considers uncertain factors in production systems given that uncertainties are major reasons for PQT constraint violation. These uncertain factors include job arrival, processing time, and machine breakdown/repair. To cope with these uncertain factors, the BPAC control decisions change dynamically with the real-time machine health and WIP distribution. The performance of BPAC is validated using discrete event simulation, and the simulation results confirm the significant performance improvement in a wide range of batch production environments.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
具有下游队列时间约束的批处理随机调度
研究了具有批处理和过程队列时间(PQT)约束的生产系统中的随机动态调度问题。生产系统由上游批量加工机器和下游单台加工机器组成。在PQT约束下,下游队列中的等待时间受到上限的约束,违反该约束会导致作业报废。批处理增加了违反PQT约束的概率,因为在批处理的服务完成后,一批在制品(WIPs)将同时进入下游队列,并承受更高的等待时间方差。利用马尔可夫决策过程建立了批量过程接纳控制(BPAC)模型,以最小化长期平均等待成本和报废成本之和。鉴于不确定性是PQT约束违反的主要原因,所提出的BPAC模型明确考虑了生产系统中的不确定性因素。这些不确定因素包括工作到达、处理时间和机器故障/维修。为了应对这些不确定因素,BPAC控制决策随着实时机器运行状况和在制品分布而动态变化。使用离散事件仿真验证了BPAC的性能,仿真结果证实了在广泛的批量生产环境中性能的显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
IEEE Transactions on Semiconductor Manufacturing
IEEE Transactions on Semiconductor Manufacturing 工程技术-工程:电子与电气
CiteScore
5.20
自引率
11.10%
发文量
101
审稿时长
3.3 months
期刊介绍: The IEEE Transactions on Semiconductor Manufacturing addresses the challenging problems of manufacturing complex microelectronic components, especially very large scale integrated circuits (VLSI). Manufacturing these products requires precision micropatterning, precise control of materials properties, ultraclean work environments, and complex interactions of chemical, physical, electrical and mechanical processes.
期刊最新文献
2024 Index IEEE Transactions on Semiconductor Manufacturing Vol. 37 Front Cover Editorial Table of Contents IEEE Transactions on Semiconductor Manufacturing Publication Information
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1