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Summary: On Bridging the Gap Between Stochastic
Integer Programming and MIP Solver Technologies
Gyana R. Parija · Shabbir Ahmed · Alan J. King
Mathematical Sciences Department, IBM T.J. Watson Research Center,
Yorktown Heights, NY 10598, USA.
School of Industrial & Systems Engineering, Georgia Institute of Technology,
765 Ferst Drive, Atlanta, GA 30332, USA.
Mathematical Sciences Department, IBM T.J. Watson Research Center,
Yorktown Heights, NY 10598, USA.
parija@us.ibm.com · sahmed@isye.gatech.edu · kingaj@us.ibm.com
April 12, 2001
Revised: April 30, 2002, October 3, 2002
Stochastic integer programs (SIP) represent a very difficult class of optimization problems arising
from the presence of both uncertainty and discreteness in planning and decision problems. Although
applications of SIP are abundant, nothing is available in the way of computational software. On
the other hand, commercial software packages for solving deterministic integer programs have been
around for quite a few years, and more recently, a package for solving stochastic linear programs has
been released. In this paper, we describe how these software tools can be integrated and exploited
for the effective solution of general purpose SIPs. We demonstrate these ideas on four problem
classes from the literature, and show significant computational advantages.
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