Neur2SP: Neural Two-Stage Stochastic Programming
Justin Dumouchelle*
Rahul Patel*
Elias B. Khalil
Merve Bodur

Department of Mechanical and Industrial Engineering, University of Toronto
khalil@mie.utoronto.ca
* Equal contribution

[Paper]  
[GitHub]  
[Video]  
[Slides]  
[Poster]  


Overview of Neur2SP.

Abstract

Stochastic Programming is a powerful modeling framework for decision-making under uncertainty. In this work, we tackle two-stage stochastic programs (2SPs), the most widely used class of stochastic programming models. Solving 2SPs exactly requires optimizing over an expected value function that is computationally intractable. Having a mixed-integer linear program (MIP) or a nonlinear program (NLP) in the second stage further aggravates the intractability, even when specialized algorithms that exploit problem structure are employed. Finding high-quality (first-stage) solutions -- without leveraging problem structure -- can be crucial in such settings. We develop Neur2SP, a new method that approximates the expected value function via a neural network to obtain a surrogate model that can be solved more efficiently than the traditional extensive formulation approach. Neur2SP makes no assumptions about the problem structure, in particular about the second-stage problem, and can be implemented using an off-the-shelf MIP solver. Our extensive computational experiments on four benchmark 2SP problem classes with different structures (containing MIP and NLP second-stage problems) demonstrate the efficiency (time) and efficacy (solution quality) of Neur2SP. In under 1.66 seconds, Neur2SP finds high-quality solutions across all problems even as the number of scenarios increases, an ideal property that is difficult to have for traditional 2SP solution techniques. Namely, the most generic baseline method typically requires minutes to hours to find solutions of comparable quality.


[Video]



[Poster]



Paper and Supplementary Material

Dumouchelle, Patel, Khalil, Bodur.
Neur2SP: Neural Two-Stage Stochastic Programming.
In NeurIPS, 2022.
(hosted on ArXiv)


[Bibtex]

Acknowledgements

  • Bodur would like to acknowledge support from an NSERC Discovery Grant.
  • Dumouchelle, Patel, and Khalil acknowledge support from the Scale AI Research Chair Program and an NSERC Discovery Grant.
  • Website template adapted from here.