A Hybrid Benders’ Decomposition Method for Solving Stochastic Constraint Programs with Linear Recourse
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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.
KeywordsConstraint Program Master Problem Chance Constraint News Vendor Problem Stochastic Constraint
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