A survey on benders decomposition applied to fixed-charge network design problems
Computers and Operations Research
Efficient Circulation of Railway Rolling Stock
Transportation Science
Circulation of railway rolling stock: a branch-and-price approach
Computers and Operations Research
Benders decomposition for the uncapacitated multiple allocation hub location problem
Computers and Operations Research
Computers and Operations Research
Computers and Operations Research
Benders Decomposition for Hub Location Problems with Economies of Scale
Transportation Science
Multiple allocation hub-and-spoke network design under hub congestion
Computers and Operations Research
A hybrid algorithm for capacitated plant location problem
Expert Systems with Applications: An International Journal
Estimates on rolling stock and crew in DSB S-tog based on timetables
ATMOS'04 Proceedings of the 4th international Dagstuhl, ATMOS conference on Algorithmic approaches for transportation modeling, optimization, and systems
Rotation planning of locomotive and carriage groups with shared capacities
ATMOS'04 Proceedings of the 4th international Dagstuhl, ATMOS conference on Algorithmic approaches for transportation modeling, optimization, and systems
Practical enhancements to the Magnanti-Wong method
Operations Research Letters
Models and algorithms for the train unit assignment problem
ISCO'12 Proceedings of the Second international conference on Combinatorial Optimization
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The problem of assigning locomotives and cars to trains is a complex task for most railways. In this paper, we propose a multicommodity network flow-based model for assigning locomotives and cars to trains in the context of passenger transportation. The model has a convenient structure that facilitates the introduction of maintenance constraints, car switching penalties, and substitution possibilities. The large integer programming formulation is solved by a branch-and-bound method that relaxes some of the integrality constraints. At each node of the tree, a mixed-integer problem is solved by a Benders decomposition approach in which the LP relaxations of multicommodity network flow problems are optimized either by the simplex algorithm or by Dantzig-Wolfe decomposition. Some computational refinements, such as the generation of Pareto-optimal cuts, are proposed to improve the performance of the algorithm. Computational experiments performed on two sets of data from a railroad show that the approach can be used to produce optimal solutions to complex problems.