Optimal clustering of railroad track maintenance jobs

Fan Peng, Yanfeng Ouyang

Research output: Contribution to journalArticle


Railroad job clustering is an important part of railroad track maintenance planning. It focuses on clustering track maintenance jobs into projects, so that the projects can be assigned to the production teams and scheduled in the planning horizon. The real-world instances of job-clustering problem usually have a very large scale, involving thousands of jobs per year. Various difficult side constraints such as mutual exclusion constraints and rounding constraints further increase the difficulty in solving the problem. In this article, we develop a mixed-integer mathematical programming model in the form of vehicle routing problem with side constraints, and propose a set of integrated heuristic algorithms to solve the problem. The proposed model and algorithms are shown to be effective and have been adopted by a Class-I railroad to help their practical operations for a few years.

Original languageEnglish (US)
Pages (from-to)235-247
Number of pages13
JournalComputer-Aided Civil and Infrastructure Engineering
Issue number4
StatePublished - Apr 1 2014

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Computational Theory and Mathematics

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