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The railway line frequency and size setting problem

The railway line frequency and size setting problem The problem studied in this paper takes as input data a set of lines forming a railway network, and an origin–destination (OD) matrix. The OD pairs may use either the railway network or an alternative transportation mode. The objective is to determine the frequency/headway of each line as well as its number of carriages, so that the net profit of the railway network is maximized. We propose a mixed integer non-linear programming formulation for this problem. Because of the computational intractability of this model, we develop four algorithms: a mixed integer linear programming (MIP) model, a MIP-based iterative algorithm, a shortest-path based algorithm, and a local search. These four algorithms are tested and compared over a set of randomly generated instances. An application over a case study shows that only the local search heuristic is capable of dealing with large instances. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Public Transport Springer Journals

The railway line frequency and size setting problem

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References (15)

Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer-Verlag Berlin Heidelberg
Subject
Business and Management; Operation Research/Decision Theory; Automotive Engineering; Computer-Aided Engineering (CAD, CAE) and Design; Transportation
ISSN
1866-749X
eISSN
1613-7159
DOI
10.1007/s12469-017-0154-2
Publisher site
See Article on Publisher Site

Abstract

The problem studied in this paper takes as input data a set of lines forming a railway network, and an origin–destination (OD) matrix. The OD pairs may use either the railway network or an alternative transportation mode. The objective is to determine the frequency/headway of each line as well as its number of carriages, so that the net profit of the railway network is maximized. We propose a mixed integer non-linear programming formulation for this problem. Because of the computational intractability of this model, we develop four algorithms: a mixed integer linear programming (MIP) model, a MIP-based iterative algorithm, a shortest-path based algorithm, and a local search. These four algorithms are tested and compared over a set of randomly generated instances. An application over a case study shows that only the local search heuristic is capable of dealing with large instances.

Journal

Public Transport – Springer Journals

Published: Feb 9, 2017

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