View in EDS

A MAS architecture for dynamic, realtime rescheduling and learning applied to railway transportation.

Saved in:
Bibliographic Details
Title: A MAS architecture for dynamic, realtime rescheduling and learning applied to railway transportation.
Authors: Narayanaswami, Sundaravalli1 sv.iitb@yahoo.com, Rangaraj, Narayan2 narayan.rangaraj@iitb.ac.in
Source: Expert Systems with Applications. Apr2015, Vol. 42 Issue 5, p2638-2656. 19p.
Subjects: MAS (Computer system), Computer architecture, Real-time computing, Computer scheduling, Machine learning, Railroad traffic
Abstract: Rescheduling disrupted railway traffic is computationally hard even for small problem instances. Disruptions may not be known beforehand and can manifest themselves even when trains are en-route, and they are usually resolved by human experts. Wide geographical distribution, a dynamically changing environment, complex interdependencies between multiple components, operational criticality and uncertainty being characteristic of railway transportation, human resolutions are inconsistent, scale-inefficient and potentially infeasible with deadlocks. We present a multi-agent system (MAS) model for dynamic and real-time rescheduling (DRR) of bi-directional railway traffic on a single track in this paper. A computational framework to dynamically dispatch the disrupted trains in real-time, based on instantaneous system parameters and to reschedule conflicting trains with inherent deadlock avoidance is incorporated in the agents’ model. A learning architecture is implemented as a proof-of concept to resolve disruptions quickly and to enhance autonomy. The model is evaluated against integer optimal solutions generated by a Mixed-Integer Linear Programming (MILP) model using realistic data. Detailed discussions on architecture, implementation using JADE (Java Agent DEvelopment) toolkit, experimental results, performance analysis, evaluation of the model, insights and limitations are reported. The numerical performance measures of the model are total weighted delay of all trains at their destination terminals and computational time for resolution. The distinguishing research contributions in this paper are a MAS architecture for railway rescheduling, dynamic dispatch priority assignment using bidding and a learning procedure that enhances autonomy. [ABSTRACT FROM AUTHOR]
Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 100062798
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A MAS architecture for dynamic, realtime rescheduling and learning applied to railway transportation.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Narayanaswami%2C+Sundaravalli%22">Narayanaswami, Sundaravalli</searchLink><relatesTo>1</relatesTo><i> sv.iitb@yahoo.com</i><br /><searchLink fieldCode="AR" term="%22Rangaraj%2C+Narayan%22">Rangaraj, Narayan</searchLink><relatesTo>2</relatesTo><i> narayan.rangaraj@iitb.ac.in</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Expert+Systems+with+Applications%22">Expert Systems with Applications</searchLink>. Apr2015, Vol. 42 Issue 5, p2638-2656. 19p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22MAS+%28Computer+system%29%22">MAS (Computer system)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+architecture%22">Computer architecture</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+scheduling%22">Computer scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Railroad+traffic%22">Railroad traffic</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Rescheduling disrupted railway traffic is computationally hard even for small problem instances. Disruptions may not be known beforehand and can manifest themselves even when trains are en-route, and they are usually resolved by human experts. Wide geographical distribution, a dynamically changing environment, complex interdependencies between multiple components, operational criticality and uncertainty being characteristic of railway transportation, human resolutions are inconsistent, scale-inefficient and potentially infeasible with deadlocks. We present a multi-agent system (MAS) model for dynamic and real-time rescheduling (DRR) of bi-directional railway traffic on a single track in this paper. A computational framework to dynamically dispatch the disrupted trains in real-time, based on instantaneous system parameters and to reschedule conflicting trains with inherent deadlock avoidance is incorporated in the agents’ model. A learning architecture is implemented as a proof-of concept to resolve disruptions quickly and to enhance autonomy. The model is evaluated against integer optimal solutions generated by a Mixed-Integer Linear Programming (MILP) model using realistic data. Detailed discussions on architecture, implementation using JADE (Java Agent DEvelopment) toolkit, experimental results, performance analysis, evaluation of the model, insights and limitations are reported. The numerical performance measures of the model are total weighted delay of all trains at their destination terminals and computational time for resolution. The distinguishing research contributions in this paper are a MAS architecture for railway rescheduling, dynamic dispatch priority assignment using bidding and a learning procedure that enhances autonomy. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=100062798
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.eswa.2014.11.013
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 19
        StartPage: 2638
    Subjects:
      – SubjectFull: MAS (Computer system)
        Type: general
      – SubjectFull: Computer architecture
        Type: general
      – SubjectFull: Real-time computing
        Type: general
      – SubjectFull: Computer scheduling
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Railroad traffic
        Type: general
    Titles:
      – TitleFull: A MAS architecture for dynamic, realtime rescheduling and learning applied to railway transportation.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Narayanaswami, Sundaravalli
      – PersonEntity:
          Name:
            NameFull: Rangaraj, Narayan
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Text: Apr2015
              Type: published
              Y: 2015
          Identifiers:
            – Type: issn-print
              Value: 09574174
          Numbering:
            – Type: volume
              Value: 42
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Expert Systems with Applications
              Type: main
ResultId 1