Credibility-based rescheduling model in a double-track railway network

A fuzzy reliable optimization approach

Lixing Yang, Xuesong Zhou, Ziyou Gao

Research output: Contribution to journalArticle

59 Citations (Scopus)

Abstract

Using a space-time network to represent the choice of train trajectories, this paper proposes a fuzzy optimization framework to reschedule trains in a double-track railway network when the capacity reduction is caused by a low-probability incident. We explicitly introduce a fuzzy variable-based recovery time to capture the uncertainty of incident duration based on professional judgements or empirical estimates. The problem is then formulated as a credibilistic two-stage fuzzy 0-1 integer optimization model to find a reliable operational plan for emergency response guidance. The first stage of the model aims to generate a traversing order on the incident link, and meanwhile the second stage adaptively generates optimized schedules to evaluate the operational plan. Crisp equivalents of mathematical models are further investigated to simplify solution methodologies. The numerical experiments, which are solved by using the GAMS optimization software, demonstrate the effectiveness and efficiency of the proposed approaches.

Original languageEnglish (US)
Pages (from-to)75-93
Number of pages19
JournalOmega (United Kingdom)
Volume48
DOIs
StatePublished - 2014

Fingerprint

Railway
Incidents
Rescheduling
Credibility
Train
Guidance
Numerical experiment
Optimization model
Integer
Trajectory
Methodology
Software
Mathematical model
Uncertainty
Schedule
Professional judgment
Emergency response
Lower probabilities

Keywords

  • Fuzzy variable
  • Space-time network
  • Train rescheduling

ASJC Scopus subject areas

  • Strategy and Management
  • Information Systems and Management
  • Management Science and Operations Research

Cite this

Credibility-based rescheduling model in a double-track railway network : A fuzzy reliable optimization approach. / Yang, Lixing; Zhou, Xuesong; Gao, Ziyou.

In: Omega (United Kingdom), Vol. 48, 2014, p. 75-93.

Research output: Contribution to journalArticle

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