Publication Type
Journal Article
Version
acceptedVersion
Publication Date
11-2016
Abstract
This paper is motivated by the following question: How to construct good approximation for the distribution of the solution value to linear optimization problem when the random objective coefficients follow a multivariate normal distribution? Using Stein’s Identity, we show that the least squares normal approximation of the random optimal value can be computed by estimating the persistency values of the corresponding optimization problem. We further extend our method to construct a least squares quadratic estimator to improve the accuracy of the approximation; in particular, to capture the skewness of the objective. Computational studies show that the new approach provides more accurate estimates of the distributions of project completion times compared to existing methods.
Keywords
Distribution Approximation, Persistency, Stein's Identity, Project Management, Statistical Timing Analysis
Discipline
Business | Operations and Supply Chain Management
Research Areas
Operations Management
Publication
Operations Research
Volume
64
Issue
6
First Page
1406
Last Page
1421
ISSN
0030-364X
Identifier
10.1287/opre.2016.1528
Publisher
INFORMS
Embargo Period
9-30-2017
Citation
ZHENG, Zhichao; Natarajan, Karthik; and TEO, Chung-Piaw.
Least squares approximation to the distribution of project completion times with Gaussian uncertainty. (2016). Operations Research. 64, (6), 1406-1421.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/5126
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1287/opre.2016.1528