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. © 2011 Springer-Verlag.
CITATION STYLE
Bessas, A., Kontogiannis, S., & Zaroliagis, C. (2011). Robust line planning in case of multiple pools and disruptions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6595 LNCS, pp. 33–44). https://doi.org/10.1007/978-3-642-19754-3_6
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