Stochastic programming model for production planning with stochastic aggregate demand and spreadsheet-based solution heuristics

IF 0.7 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Operations Research and Decisions Pub Date : 2021-01-01 DOI:10.37190/ord210406
Nasreddine Saadouli
{"title":"Stochastic programming model for production planning with stochastic aggregate demand and spreadsheet-based solution heuristics","authors":"Nasreddine Saadouli","doi":"10.37190/ord210406","DOIUrl":null,"url":null,"abstract":"The production planning problem with stochastic aggregate demand is considered in the article. By discretising the stochastic demand, a deterministic nonlinear programming formulation is developed. Then, a hybrid simulation-optimisation heuristic that capitalises on the nature of the problem is designed. The outcome is an evaluation problem that is efficiently solved using a spreadsheet model. The main contribution of the paper is providing production managers with a tractable formulation of the production planning problem in a stochastic environment and an efficient solution scheme. A key benefit of this approach is that it provides quick near-optimal solutions without requiring in-depth knowledge or significant investments in optimisation techniques and software.","PeriodicalId":43244,"journal":{"name":"Operations Research and Decisions","volume":"1 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Operations Research and Decisions","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37190/ord210406","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
引用次数: 0

Abstract

The production planning problem with stochastic aggregate demand is considered in the article. By discretising the stochastic demand, a deterministic nonlinear programming formulation is developed. Then, a hybrid simulation-optimisation heuristic that capitalises on the nature of the problem is designed. The outcome is an evaluation problem that is efficiently solved using a spreadsheet model. The main contribution of the paper is providing production managers with a tractable formulation of the production planning problem in a stochastic environment and an efficient solution scheme. A key benefit of this approach is that it provides quick near-optimal solutions without requiring in-depth knowledge or significant investments in optimisation techniques and software.
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
随机总需求生产计划的随机规划模型及基于电子表格的求解启发式方法
研究了具有随机总需求的生产计划问题。通过对随机需求的离散化,建立了确定的非线性规划公式。然后,设计了一个利用问题本质的混合模拟-优化启发式算法。结果是使用电子表格模型有效地解决了一个评估问题。本文的主要贡献在于为生产管理者提供了随机环境下生产计划问题的简便表述和有效的解决方案。这种方法的一个关键好处是,它提供了快速接近最优的解决方案,而不需要深入的知识或在优化技术和软件方面的重大投资。
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
Operations Research and Decisions
Operations Research and Decisions OPERATIONS RESEARCH & MANAGEMENT SCIENCE-
CiteScore
1.00
自引率
25.00%
发文量
16
审稿时长
15 weeks
期刊最新文献
The use of rank and optimisation methods in strategic management in higher education Frequentist inference on traffic intensity of M/M/1 queuing system Some equations to identify the threshold value in the DEMATEL method Characterisation of some generalized continuous distributions by doubly truncated moments Relationship marketing orientation in healthcare organisations with the AHP. Internal and external customer perspective
×
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