A ship egress analysis method using spectral Markov decision processes

Austin Kana, D.J. Singer

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

2 Citations (Scopus)
45 Downloads (Pure)

Abstract

This paper introduces a means of performing a ship egress analysis by applying eigenvalue analysis to the ship-centric Markov decision process (SC-MDP) framework. This method focuses on how people egress, the decisions they make under uncertainty, and the interaction between the individuals and the layout of the vessel. The objective is to understand the implications of uncertain decision making of people on general arrangement design. One metric is introduced defined as the ratio between the largest eigenvalue and the second largest. This
decision metric is used to identify and quantify changes in decisions, as well as to help identify system attributes driving those changes in decisions. A case study is presented showing the utility of this method on a ship egress problem. Sensitivity studies are performed examining the affect of uncertainty and rewards on individuals’ decision making behavior
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
  • egress analysis
  • Markov decision process
  • eigenvalue analysis

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