.png){kind=icon}
返回
Distributionally robust mixed integer linear programs: Persistency models with applications
.png){kind=icon}
OA
AI
DOI：10.1016/j.ejor.2013.07.009.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we review recent advances in the distributional analysis of mixed integer linear programs with random objective coefficients. Suppose that the probability distribution of the objective coefficients is incompletely specified and characterized through partial moment information. Conic programming methods have been recently used to find distributionally robust bounds for the expected optimal value of mixed integer linear programs over the set of all distributions with the given moment information. These methods also provide additional information on the probability that a binary variable attains a value of 1 in the optimal solution for 0-1 integer linear programs. This probability is defined as the persistency of a binary variable. In this paper, we provide an overview of the complexity results for these models, conic programming formulations that are readily implementable with standard solvers and important applications of persistency models. The main message that we hope to convey through this review is that tools of conic programming provide important insights in the probabilistic analysis of discrete optimization problems. These tools lead to distributionally robust bounds with applications in activity networks, vertex packing, discrete choice models, random walks and sequencing problems, and newsvendor problems. (C) 2013 Elsevier BM. All rights reserved.
Keyword:
Distributionally robust bounds
Mixed integer linear program
Conic program
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6
论文数:
2.2W
被引数:
6.4W
机构
引用论文
Tetrahydrocannabinol in Behavioral Disturbances in Dementia: A Crossover Randomized Controlled Trial
.png){kind=icon}
PREAI
分享收藏分享收藏Efficient Inter-node MPI Communication Using GPUDirect RDMA for InfiniBand Clusters with NVIDIA GPUs
PREAI
分享收藏Toward Single-epoch 10-centimeter Precise Point Positioning using Galileo E1/E5a and E6 Signals
PREAI
分享收藏Factor Analytic Models: Viewing the Structure of an Assessment Instrument From Three PerspectivesOAAI
OAAI分享收藏
查看更多内容