Maintenance Location Routing for Rolling Stock Under Line and Fleet Planning Uncertainty

Denise D. Tönissen (), Joachim J. Arts () and Zuo-Jun (Max) Shen ()
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Denise D. Tönissen: School of Business and Economics, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands
Joachim J. Arts: Luxembourg Centre for Logistics and Supply Chain Management, University of Luxembourg, L-1511 Luxembourg City, Luxembourg; School of Industrial Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, Netherlands
Zuo-Jun (Max) Shen: Department of Industrial Engineering and Operations Research, Department of Civil and Environmental Engineering, Tsinghua-Berkeley Shenzhen Institute, University of California, Berkeley, Berkeley, California 94720

Transportation Science, 2019, vol. 53, issue 5, 1252–1270

Abstract: Rolling stock needs regular maintenance in a maintenance facility. Rolling stock from different fleets are routed to maintenance facilities by interchanging the destinations of trains at common stations and by using empty drives. We consider the problem of locating maintenance facilities in a railway network under uncertain or changing line planning, fleet planning, and other uncertain factors. These uncertainties and changes are modeled by a discrete set of scenarios. We show that this new problem is NP-hard and provide a two-stage stochastic programming and a two-stage robust optimization formulation. The second-stage decision is a maintenance routing problem with similarity to a minimum cost-flow problem. We prove that the facility location decisions remain unchanged under a simplified routing problem, and this gives rise to an efficient mixed-integer programming (MIP) formulation. This result also allows us to find an efficient decomposition algorithm for the robust formulation based on scenario addition (SA). Computational work shows that our improved MIP formulation can efficiently solve instances of industrial size. SA improves the computational time for the robust formulation even further and can handle larger instances due to more efficient memory usage. Finally, we apply our algorithms on practical instances of the Netherlands Railways and give managerial insights.

Keywords: two-stage optimization; column-and-constraint generation; facility location; rolling stock; maintenance routing (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (8)

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