Train service timetabling in railway open markets by particle swarm optimisation

Ho, T.K., Tsang, C.W., Ip, K.H., & Kwan, K.S. (2011) Train service timetabling in railway open markets by particle swarm optimisation. Expert Systems with Applications, 39(1), pp. 861-868.

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Railway timetabling is an important process in train service provision as it matches the transportation demand with the infrastructure capacity while customer satisfaction is also considered. It is a multi-objective optimisation problem, in which a feasible solution, rather than the optimal one, is usually taken in practice because of the time constraint. The quality of services may suffer as a result. In a railway open market, timetabling usually involves rounds of negotiations among a number of self-interested and independent stakeholders and hence additional objectives and constraints are imposed on the timetabling problem. While the requirements of all stakeholders are taken into consideration simultaneously, the computation demand is inevitably immense. Intelligent solution-searching techniques provide a possible solution. This paper attempts to employ a particle swarm optimisation (PSO) approach to devise a railway timetable in an open market. The suitability and performance of PSO are studied on a multi-agent-based railway open-market negotiation simulation platform.

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9 citations in Scopus
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7 citations in Web of Science®

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ID Code: 46114
Item Type: Journal Article
Refereed: Yes
Keywords: Intelligent Transportation Systems, Railway Open Markets, Timetabling, Particle Swarm Optimisation
DOI: 10.1016/j.eswa.2011.07.084
ISSN: 0957-4174
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Transport Engineering (090507)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2011 Elsevier Ltd. All rights reserved.
Deposited On: 25 Sep 2011 23:21
Last Modified: 29 Jan 2012 22:50

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