Decision-making of online rescheduling procedures using neuroevolution of augmenting topologies

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Abstract

Online scheduling requires appropriate timing of rescheduling procedures, as well as the determination of relevant horizon length. Optimal choices of these quantities are highly dependent on the uncertainty of the scheduling environment and may vary over time. We propose an approach where a neural network is trained to make online decisions on these quantities, as well as on the choice of the rescheduling method (mathematical programming or metaheuristics). In our approach, the neural network is trained using neuroevolution of augmenting topologies (NEAT) in a simulated environment. In this paper, we also optimize the rescheduling interval and horizon length of a conventional periodically occurring rescheduling on a dynamic routing problem. The resulting approach is the baseline for the development of the proposed neural network approach.
Original languageEnglish
Title of host publicationProceedings of the 29th European Symposium on Computer Aided Chemical Engineering
EditorsAnton Kiss, Edwin Zondervan, Richard Lakerveld, Leyla Özkan
PublisherElsevier
Pages1177-1182
Volume46
ISBN (Print)9780128186343
DOIs
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventEuropean Symposium on Computer-Aided Process Engineering - Eindhoven, Netherlands
Duration: 16 Jun 201919 Jun 2019
Conference number: 29
https://escape29.nl/

Publication series

NameComputer-aided chemical engineering
PublisherElsevier
Volume46
ISSN (Electronic)1570-7946

Conference

ConferenceEuropean Symposium on Computer-Aided Process Engineering
Abbreviated titleESCAPE
CountryNetherlands
CityEindhoven
Period16/06/201919/06/2019
Internet address

Keywords

  • online scheduling
  • horizon length
  • scheduling interval
  • neural network
  • NEAT

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