Abstract
We consider the line planning problem in public transportation, under a robustness perspective. We present a mechanism for robust line planning in the case of multiple line pools, when the line operators have a different utility function per pool. We conduct an experimental study of our mechanism on both synthetic and real-world data that shows fast convergence to the optimum. We also explore a wide range of scenarios, varying from an arbitrary initial state (to be solved) to small disruptions in a previously optimal solution (to be recovered). Our experiments with the latter scenario show that our mechanism can be used as an online recovery scheme causing the system to re-converge to its optimum extremely fast.
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Bessas, A., Kontogiannis, S., Zaroliagis, C. (2011). Robust Line Planning in Case of Multiple Pools and Disruptions. In: Marchetti-Spaccamela, A., Segal, M. (eds) Theory and Practice of Algorithms in (Computer) Systems. TAPAS 2011. Lecture Notes in Computer Science, vol 6595. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19754-3_6
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DOI: https://doi.org/10.1007/978-3-642-19754-3_6
Publisher Name: Springer, Berlin, Heidelberg
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