Publication Type

Journal Article

Version

Postprint

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

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1287/opre.2016.1528

Available for download on Saturday, September 30, 2017

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