Machine Learning-Based Problem Space Reduction in Stochastic Programming Models: An Application in Biofuel Supply Chain Network Design

Abstract

Biofuels derived from feedstock offer a sustainable source for meeting energy needs. The design of supply chains that deliver these fuels needs to consider quality variability with special attention to shipping costs, because biofuel feedstocks are voluminous. Stochastic programming models that consider all these considerations incur a heavy computational burden. The present work proposes a hybrid strategy that leverages machine learning to reduce the computational complexity of stochastic programming models via problem space reduction. First, numerous randomly generated reduced-space versions of the problem are solved multiple times to generate a set of solution data based on the concept of bootstrapping. Next, a supervised machine learning algorithm is implemented to predict a potentially beneficial mixed integer linear program problem space from which a near-optimal solution can be obtained. Finally, the mixed integer linear program selects the optimal solution from the reduced space generated by the machine learning algorithm. Through extensive numerical experimentation, we determine how much the problem space can be reduced, how many times the reduced space problem needs to be solved and the best performing machine learning techniques for this application. Several supervised learning algorithms, including logistic regression, decision tree, random forest, support vector machine, and k-nearest neighbors, are evaluated. The numerical experiments demonstrate that our proposed solution procedure yields near-optimal outcomes with a considerably reduced computational burden.