当前位置: X-MOL 学术Optim. Methods Softw. › 论文详情
Fast scenario reduction by conditional scenarios in two-stage stochastic MILP problems
Optimization Methods & Software ( IF 1.431 ) Pub Date : 2019-12-03 , DOI: 10.1080/10556788.2019.1697696
C. Beltran-Royo

A common approach to model stochastic programming problems is based on scenarios. An option to manage the difficulty of these problems corresponds to reduce the original set of scenarios. In this paper we study a new fast scenario reduction method based on Conditional Scenarios (CS). We analyse the degree of similarity between the original large set of scenarios and the small set of conditional scenarios in terms of the first two moments. In our numerical experiment, based on the stochastic capacitated facility location problem, we compare two fast scenario reduction methods: the CS method and the Monte Carlo (MC) method. The empirical conclusion is twofold: On the one hand, the achieved expected costs obtained by the two approaches are similar, although the MC method obtains a better approximation to the original set of of scenarios in terms of the moment matching criterion. On the other hand, the CS approach outperforms the MC approach with the same number of scenarios in terms of solution time.
更新日期:2019-12-03

 

全部期刊列表>>
材料学研究精选
Springer Nature Live 产业与创新线上学术论坛
胸腔和胸部成像专题
自然科研论文编辑服务
ACS ES&T Engineering
ACS ES&T Water
屿渡论文,编辑服务
杨超勇
周一歌
华东师范大学
段炼
清华大学
中科大
唐勇
跟Nature、Science文章学绘图
隐藏1h前已浏览文章
中洪博元
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
x-mol收录
福州大学
南京大学
王杰
左智伟
电子显微学
何凤
洛杉矶分校
吴杰
赵延川
试剂库存
天合科研
down
wechat
bug