A decision-making framework for planning lifecycle ballast water treatment compliance

Austin Kana, D.C. Brefort, H.C. Seyffert, D.J. Singer

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
35 Downloads (Pure)

Abstract

This paper introduces a novel decision-making framework for planning lifecycle compliance of ballast water treatment by applying eigenvalue spectral analysis
to the ship-centric Markov decision process (SC-MDP) framework. This method focuses on identifying the relationships of various decision making scenarios, and how those relationships change through time. The objective is to understand both these relationships and the impact of initial technology selection on lifecycle ballast water compliance. Two metrics are used. First, the optimal lifecycle strategy is presented for technology selection. Second,
the set of dominant eigenvalues is used as a metric to identify the number of unique, initial condition dependent design absorbing paths the process may converge to. Sensitivity studies are performed examining the affect of policy strength on preferred compliance strategy.
Original languageEnglish
Title of host publicationProceedings of the 13th International Symposium on Practical Design of Ships and Other Floating Structures (PRADS2016)
EditorsU.D. Nielsen, J.J. Jensen et al
Place of PublicationCopenhagen, Denmark
PublisherPRADS Organising Committee
Number of pages8
ISBN (Print)978-87-7475-473-2
Publication statusPublished - 2016
EventInternational Symposium on Practical Design of Ships and Other Floating Structures, PRADS'2016 - Copenhagen, Denmark
Duration: 4 Sep 20168 Sep 2016
Conference number: 13
http://www.prads2016.dk/

Conference

ConferenceInternational Symposium on Practical Design of Ships and Other Floating Structures, PRADS'2016
Abbreviated titlePRADS'2016
CountryDenmark
CityCopenhagen
Period4/09/168/09/16
Internet address

Keywords

  • Ship design
  • decision making
  • Ballast water compliance
  • Markov decision process
  • eigenvalue analysis

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