A simulation tool for combined rail/road transport in intermodal terminals
Mathematics and Computers in Simulation - Selected papers of the MSSANZ/IMACS 13th biennial conference on modelling and simulation, Hamilton, New Zealand, December 1999
TOPSU - RDM a simulation platform for online railway delay management
Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops
Integer programming approaches for solving the delay management problem
ATMOS'04 Proceedings of the 4th international Dagstuhl, ATMOS conference on Algorithmic approaches for transportation modeling, optimization, and systems
The computational complexity of delay management
WG'05 Proceedings of the 31st international conference on Graph-Theoretic Concepts in Computer Science
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Delays in a railway network are a common problem that railway companies face in their daily operations. When a train is delayed, it may either be beneficial to let a connecting train wait so that passengers in the delayed train do not miss their connection, or it may be beneficial to let the connecting train depart on time to avoid further delays. These decisions naturally depend on the global structure of the network, on the schedule, on the passenger routes and on the imposed delays. The railway delay management (RDM) problem (in a broad sense) is to decide which trains have to wait for connecting trains and which trains have to depart on time. The offline version (i.e. when all delays are known in advance) is already NP-hard for very special networks. In this paper we show that the online railway delay management (ORDM) problem is PSPACE-hard. This result justifies the need for a simulation approach to evaluate wait policies for ORDM. For this purpose we present TOPSUâ聙聰RDM, a simulation platform for evaluating and comparing different heuristics for the ORDM problem with stochastic delays. Our novel approach is to separate the actual simulation and the program that implements the decision-making policy, thus enabling implementations of different heuristics to â聙聵â聙聵competeâ聙聶â聙聶 on the same instances and delay distributions. We also report on computational results indicating the worthiness of developing intelligent wait policies. For RDM and other logistic planning processes, it is our goal to bridge the gap between theoretical models, which are accessible to theoretical analysis, but are often too far away from practice, and the methods which are used in practice today, whose performance is almost impossible to measure.