Capacitated multi-item multi-echelon lot sizing with setup carry-over under uncertain demand

IF 9.8 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL International Journal of Production Economics Pub Date : 2024-08-24 DOI:10.1016/j.ijpe.2024.109379
Manuel Schlenkrich, Sophie N. Parragh
{"title":"Capacitated multi-item multi-echelon lot sizing with setup carry-over under uncertain demand","authors":"Manuel Schlenkrich,&nbsp;Sophie N. Parragh","doi":"10.1016/j.ijpe.2024.109379","DOIUrl":null,"url":null,"abstract":"<div><p>This study focuses on the lot sizing problem with setup carry-over in multi-item multi-echelon capacitated production systems under uncertain customer demand for end items, as well as components. We investigate budget-uncertainty robust optimization and scenario-based stochastic programming, to address the uncertainty in customer demand. Three modeling strategies are proposed within the stochastic programming framework, exploring different decision stages for setup carry-over and production quantities. In our examination of the robust model, we explore different robustness parameters, specifically the uncertainty budget and the variation interval. Extensive numerical experiments are conducted to compare the average and worst case performance of the models on out-of-sample scenarios. We fit conditional inference trees to the evaluation results and predict the suitability of robust and stochastic approaches for the test instances based on their problem characteristics. The findings provide valuable insights, potentially enabling decision makers to estimate the most appropriate approach based on certain characteristics of the lot sizing problem that is addressed. Moreover they highlight the importance of choosing appropriate robustness parameters for robust optimization models and emphasize the value of flexibility in carry-over and quantity decisions.</p></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"277 ","pages":"Article 109379"},"PeriodicalIF":9.8000,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Production Economics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925527324002366","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0

Abstract

This study focuses on the lot sizing problem with setup carry-over in multi-item multi-echelon capacitated production systems under uncertain customer demand for end items, as well as components. We investigate budget-uncertainty robust optimization and scenario-based stochastic programming, to address the uncertainty in customer demand. Three modeling strategies are proposed within the stochastic programming framework, exploring different decision stages for setup carry-over and production quantities. In our examination of the robust model, we explore different robustness parameters, specifically the uncertainty budget and the variation interval. Extensive numerical experiments are conducted to compare the average and worst case performance of the models on out-of-sample scenarios. We fit conditional inference trees to the evaluation results and predict the suitability of robust and stochastic approaches for the test instances based on their problem characteristics. The findings provide valuable insights, potentially enabling decision makers to estimate the most appropriate approach based on certain characteristics of the lot sizing problem that is addressed. Moreover they highlight the importance of choosing appropriate robustness parameters for robust optimization models and emphasize the value of flexibility in carry-over and quantity decisions.

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
在需求不确定的情况下,利用设置结转确定多项目多机群的批量规模
本研究的重点是在客户对终端产品和零部件的需求不确定的情况下,在多项目多梯队产能生产系统中,如何解决带有设置结转的批量大小问题。我们研究了预算不确定性稳健优化和基于情景的随机编程,以解决客户需求的不确定性问题。我们在随机编程框架内提出了三种建模策略,探讨了设置结转和生产数量的不同决策阶段。在对稳健模型的研究中,我们探讨了不同的稳健性参数,特别是不确定性预算和变化区间。我们进行了广泛的数值实验,以比较模型在样本外情况下的平均性能和最差性能。我们将条件推理树拟合到评估结果中,并根据问题特征预测稳健方法和随机方法对测试实例的适用性。这些发现提供了有价值的见解,可能使决策者能够根据所处理的批量大小问题的某些特征来估计最合适的方法。此外,它们还强调了为稳健优化模型选择适当稳健性参数的重要性,并强调了灵活性在结转和数量决策中的价值。
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
International Journal of Production Economics
International Journal of Production Economics 管理科学-工程:工业
CiteScore
21.40
自引率
7.50%
发文量
266
审稿时长
52 days
期刊介绍: The International Journal of Production Economics focuses on the interface between engineering and management. It covers all aspects of manufacturing and process industries, as well as production in general. The journal is interdisciplinary, considering activities throughout the product life cycle and material flow cycle. It aims to disseminate knowledge for improving industrial practice and strengthening the theoretical base for decision making. The journal serves as a forum for exchanging ideas and presenting new developments in theory and application, combining academic standards with practical value for industrial applications.
期刊最新文献
Collaborative supply chain network design under demand uncertainty: A robust optimization approach Implementing intelligent manufacturing policies to increase the total factor productivity in manufacturing: Transmission mechanisms through construction of industrial chains Logistics service sharing in cross-border e-commerce Strategic interactions between manufacturer channel choice and platform entry in a dual-market system Competition and organizational structure co-optimization of OEMs in a product-service supply chain
×
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