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Optimal Clustering of Railroad Track Maintenance Jobs
Author(s) -
Peng Fan,
Ouyang Yanfeng
Publication year - 2014
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12036
Subject(s) - cluster analysis , computer science , rounding , heuristic , operations research , integer programming , track (disk drive) , class (philosophy) , routing (electronic design automation) , set (abstract data type) , mathematical optimization , time horizon , engineering , artificial intelligence , algorithm , mathematics , computer network , programming language , operating system
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.