An Investigation into Prediction + Optimisation for the Knapsack Problem

We study a prediction�+�optimisation formulation of the knapsack problem. The goal is to predict the profits of knapsack items based on historical data, and afterwards use these predictions to solve the knapsack. The key is that the item profits are not known beforehand and thus must be estimated, but the quality of the solution is evaluated with respect to the true profits. We formalise the problem, the goal of minimising expected regret and the learning problem, and investigate different machine learning approaches that are suitable for the optimisation problem. Recent methods for linear programs have incorporated the linear relaxation directly into the loss function. In contrast, we consider less intrusive techniques of changing the loss function, such as standard and multi-output regression, and learning-to-rank methods. We empirically compare the approaches on real-life energy price data and synthetic benchmarks, and investigate the merits of the different approaches.