Copado-Mendez, P, Lin, Z and Kwan, R (2018) Size Limited Iterative Method: A Hybridized Heuristic for Train Unit Scheduling Optimization. In: 14th International Conference on Advanced Systems in Public Transport (CASPT). 14th International Conference on Advanced Systems in Public Transport (CASPT), 23-25 Jul 2018, Brisbane, Australia.
Abstract
In this research is presented an hybrid approach based on heuris-
tics for solving large instances for the Train Unit Scheduling Optimization
(TUSO). TUSO has been modelled as an IntegerMulti-Commodity Flow Problem (IMCF) lay on a Directed Acyclic Graph (DAG), and solved by Integer Linear Programming (ILP). This method proceeds in a way to iteratively improve the quality of one or more given feasible solutions by solving reduced instances of the original problem where only a subset of the arcs in the DAG are heuristically chosen to be optimised. Our approach is designed for reducing the original DAG into a much smaller size, but still retaining all the essential arcs for the optimal solution as much as possible. The capabilities of this frame-work for train unit scheduling optimization are tested by real-world cases and
compared with the results from running the ILP solver alone for the original full problem instance and with the manual solutions for each dataset.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Train Unit Scheduling Optimization; Hybrid approach; Heuristics |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Funding Information: | Funder Grant number EPSRC EP/M007243/1 First Rail Holdings Ltd 4700245342 |
Depositing User: | Symplectic Publications |
Date Deposited: | 11 Sep 2019 15:04 |
Last Modified: | 11 Sep 2019 15:25 |
Status: | Published |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:150722 |