Scheduling of Railway Track Maintenance Activities and Crews

Before railway track maintenance crews can undertake any work, the allocation of activities to available time intervals in the train schedule as well as to crews must be undertaken. This paper puts forward a model aimed at determining the best allocation of maintenance activities and crews so as to...

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Bibliographic Details
Published inThe Journal of the Operational Research Society Vol. 49; no. 10; pp. 1026 - 1033
Main Author Higgins, A.
Format Journal Article
LanguageEnglish
Published Basingstoke Macmillan Press 01.10.1998
Palgrave
Taylor & Francis Ltd
Subjects
Applied sciences
Case-Oriented Papers
Decision making models
Exact sciences and technology
Ground, air and sea transportation, marine construction
Heuristics
Integer programming
Maintenance costs
Maintenance training
Musical intervals
Objective functions
Railroad maintenance
Railroad trains
Railway networks
Railway transportation and traffic
Repair & maintenance
Scheduling
Studies
Tabu search
Time windows
Transportation planning, management and economics
Australia
Integer programming
Zero one programming
Allocation
Tabu search
Scheduling
Rail transportation
Maintenance
Personnel
Algorithm
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Summary:Before railway track maintenance crews can undertake any work, the allocation of activities to available time intervals in the train schedule as well as to crews must be undertaken. This paper puts forward a model aimed at determining the best allocation of maintenance activities and crews so as to mininise the disruption to and from scheduled trains and to reduce completion time. The model is subject to constraints such as available budget; maintenance activity precedence; track availability; and minimum travel time between track links. Solution to the model is found using the tabu search heuristic for which the neighbourhood is defined by swapping the order of jobs, maintenance crews, or both. Application to a 300 km track corridor with a four day planning horizon is discussed along with the impacts of modifying the number of maintenance crews. The optimal schedule achieved an 8% reduction in expected interference delay with the train schedule and a 7% reduction in average completion times, relative to the solution which was constructed manually.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:0160-5682
1476-9360
0160-5682
DOI:10.1038/sj.jors.2600612