DeepAI AI Chat
Log In Sign Up

Fast Continuous and Integer L-shaped Heuristics Through Supervised Learning

by   Eric Larsen, et al.

We propose a methodology at the nexus of operations research and machine learning (ML) leveraging generic approximators available from ML to accelerate the solution of mixed-integer linear two-stage stochastic programs. We aim at solving problems where the second stage is highly demanding. Our core idea is to gain large reductions in online solution time while incurring small reductions in first-stage solution accuracy by substituting the exact second-stage solutions with fast, yet accurate supervised ML predictions. This upfront investment in ML would be justified when similar problems are solved repeatedly over time, for example, in transport planning related to fleet management, routing and container yard management. Our numerical results focus on the problem class seminally addressed with the integer and continuous L-shaped cuts. Our extensive empirical analysis is grounded in standardized families of problems derived from stochastic server location (SSLP) and stochastic multi knapsack (SMKP) problems available in the literature. The proposed method can solve the hardest instances of SSLP in less than 9 of SMKP the same figure is 20 than 0.1


page 1

page 2

page 3

page 4


Neur2SP: Neural Two-Stage Stochastic Programming

Stochastic programming is a powerful modeling framework for decision-mak...

Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information

This paper offers a methodological contribution at the intersection of m...

Predicting Solution Summaries to Integer Linear Programs under Imperfect Information with Machine Learning

The paper provides a methodological contribution at the intersection of ...

Using 3D-printing in disaster response: The two-stage stochastic 3D-printing knapsack problem

In this paper, we will shed light on when to pack and use 3D-printers in...

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

We propose a novel approach using supervised learning to obtain near-opt...

Machine Learning for Cutting Planes in Integer Programming: A Survey

We survey recent work on machine learning (ML) techniques for selecting ...

Learning Mixed-Integer Linear Programs from Contextual Examples

Mixed-integer linear programs (MILPs) are widely used in artificial inte...