基于智能体的仿真框架优化随机规划与人为引入的不确定性

Mohammad Ramshani, Xueping Li, Anahita Khojandi, Lorna Treffert
{"title":"基于智能体的仿真框架优化随机规划与人为引入的不确定性","authors":"Mohammad Ramshani, Xueping Li, Anahita Khojandi, Lorna Treffert","doi":"10.1109/WSC40007.2019.9004909","DOIUrl":null,"url":null,"abstract":"Uncertainty is ubiquitous in almost every real world optimization problem. Stochastic programming has been widely utilized to capture the uncertain nature of real world optimization problems in many different aspects. These models, however, often fall short in adequately capturing the stochasticity introduced by the interactions within a system or a society involving human beings or sub-systems. Agent-based modeling, on the other hand, can efficiently handle such randomness resulting from the interactions among different members or elements of a systems. In this study, we develop a framework for stochastic programming optimization by embedding an agent-based model to allow uncertainties due to both stochastic nature of system parameters as well as the interactions among the agents. A case study is presented to show the effectiveness of the proposed framework.","PeriodicalId":127025,"journal":{"name":"2019 Winter Simulation Conference (WSC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Optimization via Agent-based Simulation Framework to Integrate Stochastic Programming with Human Introduced Uncertainty\",\"authors\":\"Mohammad Ramshani, Xueping Li, Anahita Khojandi, Lorna Treffert\",\"doi\":\"10.1109/WSC40007.2019.9004909\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Uncertainty is ubiquitous in almost every real world optimization problem. Stochastic programming has been widely utilized to capture the uncertain nature of real world optimization problems in many different aspects. These models, however, often fall short in adequately capturing the stochasticity introduced by the interactions within a system or a society involving human beings or sub-systems. Agent-based modeling, on the other hand, can efficiently handle such randomness resulting from the interactions among different members or elements of a systems. In this study, we develop a framework for stochastic programming optimization by embedding an agent-based model to allow uncertainties due to both stochastic nature of system parameters as well as the interactions among the agents. A case study is presented to show the effectiveness of the proposed framework.\",\"PeriodicalId\":127025,\"journal\":{\"name\":\"2019 Winter Simulation Conference (WSC)\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Winter Simulation Conference (WSC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WSC40007.2019.9004909\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Winter Simulation Conference (WSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WSC40007.2019.9004909","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

不确定性在几乎所有现实世界的优化问题中都是普遍存在的。随机规划已被广泛应用于捕捉现实世界中许多不同方面的优化问题的不确定性。然而,这些模型在充分捕捉由涉及人类或子系统的系统或社会内部的相互作用引入的随机性方面往往不足。另一方面,基于agent的建模可以有效地处理系统中不同成员或元素之间相互作用所产生的随机性。在这项研究中,我们通过嵌入一个基于代理的模型来开发一个随机规划优化框架,以允许由于系统参数的随机性以及代理之间的相互作用而产生的不确定性。最后通过一个案例分析,证明了该框架的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Optimization via Agent-based Simulation Framework to Integrate Stochastic Programming with Human Introduced Uncertainty
Uncertainty is ubiquitous in almost every real world optimization problem. Stochastic programming has been widely utilized to capture the uncertain nature of real world optimization problems in many different aspects. These models, however, often fall short in adequately capturing the stochasticity introduced by the interactions within a system or a society involving human beings or sub-systems. Agent-based modeling, on the other hand, can efficiently handle such randomness resulting from the interactions among different members or elements of a systems. In this study, we develop a framework for stochastic programming optimization by embedding an agent-based model to allow uncertainties due to both stochastic nature of system parameters as well as the interactions among the agents. A case study is presented to show the effectiveness of the proposed framework.
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:604180095
Book学术官方微信