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arXiv · 1912.08112

A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs

Yoshua Bengio·Emma Frejinger·Andrea Lodi·Rahul Patel·Sriram Sankaranarayanan
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Abstract

We propose a novel approach using supervised learning to obtain near-optimal primal solutions for two-stage stochastic integer programming (2SIP) problems with constraints in the first and second stages. The goal of the algorithm is to predict a "representative scenario" (RS) for the problem such that, deterministically solving the 2SIP with the random realization equal to the RS, gives a near-optimal solution to the original 2SIP. Predicting an RS, instead of directly predicting a solution ensures first-stage feasibility of the solution. If the problem is known to have complete recourse, second-stage feasibility is also guaranteed. For computational testing, we learn to find an RS for a two-stage stochastic facility location problem with integer variables and linear constraints in both stages and consistently provide near-optimal solutions. Our computing times are very competitive with those of general-purpose integer programming solvers to achieve a similar solution quality.

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Yoshua Bengio, Emma Frejinger, Andrea Lodi, Rahul Patel, Sriram Sankaranarayanan. 2019-12-17. A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs. https://arxiv.org/abs/1912.08112

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