Convex Approximations of a Probabilistic Bicriteria Model with Disruptions
Loading...
Authors
Rengarajan, Tara
Dimitrov, Nedialko B.
Morton, David P.
Subjects
programming
stochastic: probabilistic constraints
simulation
programming: multiple criteria
stochastic: probabilistic constraints
simulation
programming: multiple criteria
Advisors
Date of Issue
2011
Date
Publisher
Language
Abstract
We consider a multiperiod system operation problem with two con
icting objectives, minimizing
cost and risk. Risk stems from uncertain disruptions to the system during operation.
While a general model would hedge against disruptions in each time period, we study special
cases in which only a modest number of disruptions occur. To optimize for risk, we
employ a convex approximation based on constraint sampling. We develop a strati ed sampling
scheme based on distributional information on the time of disruption. We establish
that our scheme yields signi cant savings in sampling costs|up to an order of magnitude
in the number of time periods|over naive sampling. Moreover, in the absence of distributional
information, we exhibit a sampling strategy that has comparable performance to
optimal strati cation. We numerically demonstrate that strati cation improves cost over
naive sampling, improving the solution's proximity to the e cient frontier of the bicriteria
problem.
Type
Article
Description
INFORMS Journal on Computing, July 2011
The article of record as published may be located at http://dx.doi.org/ 10.1287/ijoc.1110.0483
The article of record as published may be located at http://dx.doi.org/ 10.1287/ijoc.1110.0483
Series/Report No
Department
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Tara Rengarajan, Nedialko B. Dimitrov, and David P. Morton. Convex Approximations of a Probabilistic Bicriteria Model with Disruptions. INFORMS Journal on Computing, July 2011, doi: 10.1287/ijoc.1110.0483
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.