Modelling and solving online optimisation problems

Alexander Ek, Maria Garcia de la Banda, Andreas Schutt, Peter J. Stuckey, Guido Tack

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Many optimisation problems are of an online—also called dynamic—nature, where new information is expected to arrive and the problem must be resolved in an ongoing fashion to (a) improve or revise previous decisions and (b) take new ones. Typically, building an online decision-making system requires substantial ad-hoc coding to ensure the offline version of the optimisation problem is continually adjusted and resolved. This paper defines a general framework for automatically solving online optimisation problems. This is achieved by extending a model of the offline optimisation problem, from which an online version is automatically constructed, thus requiring no further modelling effort. In doing so, it formalises many of the aspects that arise in online optimisation problems. The same framework can be applied for automatically creating sliding-window solving approaches for problems that have a large time horizon. Experiments show we can automatically create efficient online and sliding-window solutions to optimisation problems.
Original languageEnglish
Title of host publicationThe Thirty-Fourth AAAI Conference on Artificial Intelligence
EditorsVincent Conitzer, Fei Sha
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages1477-1485
Number of pages9
Volume34
Edition2
ISBN (Electronic)9781577358350
DOIs
Publication statusPublished - 2020
EventAAAI Conference on Artificial Intelligence 2020 - New York, United States of America
Duration: 7 Feb 202012 Feb 2020
Conference number: 34th
https://aaai.org/Conferences/AAAI-20/ (Website)

Publication series

NameAAAI Conference on Artificial Intelligence
PublisherAAAI Press
Number2
Volume34
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

ConferenceAAAI Conference on Artificial Intelligence 2020
Abbreviated titleAAAI-20
CountryUnited States of America
CityNew York
Period7/02/2012/02/20
Internet address

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