Abstract
The problem of robust line planning requests for a set of origin-destination paths (lines) along with their frequencies in an underlying railway network infrastructure, which are robust to fluctuations of real-time parameters of the solution. In this work, we investigate a variant of robust line planning stemming from recent regulations in the railway sector that introduce competition and free railway markets, and set up a new application scenario: there is a (potentially large) number of line operators that have their lines fixed and operate as competing entities issuing frequency requests, while the management of the infrastructure itself remains the responsibility of a single entity, the network operator. The line operators are typically unwilling to reveal their true incentives, while the network operator strives to ensure a fair (or socially optimal) usage of the infrastructure, e.g., by maximizing the (unknown to him) aggregate incentives of the line operators.
By investigating a resource allocation mechanism (originally developed in the context of communication networks), we show that a socially optimal solution can be accomplished in certain situations via an anonymous incentive-compatible pricing scheme for the usage of the shared resources that is robust against the unknown incentives and the changes in the demands of the entities.This brings up a new notion of robustness, which we call incentive-compatible robustness, that considers as robustness of the system its tolerance to the entities’ unknown incentives and elasticity of demands, aiming at an eventual stabilization to an equilibrium point that is as close as possible to the social optimum.
This work was partially supported by the Future and Emerging Technologies Unit of EC, under contracts no. FP6-021235-2 (FP6 IST/FET Open/Project ARRIVAL), and no. ICT-215270 (FP7 ICT/FET Proactive/Project FRONTS).
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Bessas, A., Kontogiannis, S., Zaroliagis, C. (2009). Incentive-Compatible Robust Line Planning. In: Ahuja, R.K., Möhring, R.H., Zaroliagis, C.D. (eds) Robust and Online Large-Scale Optimization. Lecture Notes in Computer Science, vol 5868. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05465-5_4
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DOI: https://doi.org/10.1007/978-3-642-05465-5_4
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