.png){kind=icon}
返回
A randomized linear programming method for computing network bid prices
.png){kind=icon}
PRE
AI
DOI：10.1287/trsc.33.2.207.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We analyze a randomized version of the deterministic Linear programming (DLP) method for computing network bid prices. The method consists of simulating a sequence of realizations of itinerary demand and solving deterministic linear programs to allocate capacity, to itineraries for each realization. The dual prices from this sequence are then averaged to form a bid price approximation This randomized linear programming (RLP) method is only slightly more complicated to implement than the DLP method. We show that the RLP method can be viewed as a procedure for estimating the gradient of the expected perfect information (PI) network revenue. That is, the expected revenue obtained with full information on future demand realizations. The expected PI revenue can, in turn, be viewed as an approximation to the optimal value function. We establish conditions under which the RLP procedure provides an unbiased estimator of the gradient of the expected PI revenue. Computational tests are performed to evaluate the revenue performance of the RLP method compared to the DLP.
Keyword:
FARE CLASSES
MODEL
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:
4.8
论文数:
1.9K
被引数:
8.4K
机构
暂无机构信息
引用论文
Coexistence of YWHAZ amplification predicts better prognosis in muscle-invasive bladder cancer with CDKN2A or TP53 loss.png)
Oncotarget
OAAI
OncotargetIF0
OAAI分享收藏分享收藏 没有更多内容
没有更多内容