A hybrid benders' decomposition method for solving stochastic constraint programs with linear recourse

Authors:
S. Armagan Tarim;Ian Miguel
Affiliations:
Cork Constraint Computation Centre, University College Cork, Cork, Ireland;School of Computer Science, University of St.Andrews, St.Andrews, Scotland
Venue:
CSCLP'05 Proceedings of the 2005 Joint ERCIM/CoLogNET international conference on Constraint Solving and Constraint Logic Programming
Year:
2005

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Abstract

We adopt Benders' decomposition algorithm to solve scenario-based Stochastic Constraint Programs (SCPs) with linear recourse. Rather than attempting to solve SCPs via a monolithic model, we show that one can iteratively solve a collection of smaller sub-problems and arrive at a solution to the entire problem. In this approach, decision variables corresponding to the initial stage and linear recourse actions are grouped into two sub-problems. The sub-problem corresponding to the recourse action further decomposes into independent problems, each of which is a representation of a single scenario. Our computational experience on stochastic versions of the well-known template design and warehouse location problems shows that, for linear recourse SCPs, Benders' decomposition algorithm provides a very efficient solution method.