An Investigation into Prediction + Optimisation for the Knapsack Problem

Emir Demirović, Peter J. Stuckey, James Bailey, Jeffrey Chan, Chris Leckie, Kotagiri Ramamohanarao, Tias Guns

Research output: Chapter in Book/Report/Conference proceedingConference paperResearch

6 Citations (Scopus)
203 Downloads (Pure)

Abstract

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.

Original languageEnglish
Title of host publicationIntegration of Constraint Programming, Artificial Intelligence, and Operations Research - 16th International Conference, CPAIOR 2019, Proceedings
EditorsLouis-Martin Rousseau, Kostas Stergiou
PublisherSpringer Verlag
Pages241-257
Number of pages17
ISBN (Print)9783030192112
DOIs
Publication statusPublished - 1 Jan 2019
Event16th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2019 - Thessaloniki, Greece
Duration: 4 Jun 20197 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11494 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2019
CountryGreece
CityThessaloniki
Period4/06/197/06/19

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