Rare-event simulation for distribution networks

Document Type

Article

Publication Date

1-1-2019

Abstract

We model optimal allocations in a distribution network as the solution of a linear program (LP) that minimizes the cost of unserved demands across nodes in the network. The constraints in the LP dictate that, after a given node's supply is exhausted, its unserved demand is distributed among neighboring nodes. All nodes do the same, and the resulting solution is the optimal allocation. Assuming that the demands are random (following a jointly Gaussian law), our goal is to study the probability that the optimal cost of unserved demands exceeds a large threshold, which is a rare event. Our contribution is the development of importance sampling and conditional Monte Carlo algorithms for estimating this probability. We establish the asymptotic efficiency of our algorithms and also present numerical results that illustrate strong performance of our procedures.

Identifier

85073444961 (Scopus)

Publication Title

Operations Research

External Full Text Location

https://doi.org/10.1287/opre.2019.1852

e-ISSN

15265463

ISSN

0030364X

First Page

1383

Last Page

1396

Issue

5

Volume

67

Grant

N660011824028

Fund Ref

Defense Advanced Research Projects Agency

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