In this paper we address optimal routing problems in networks where travel times are both stochastic and time-dependent. In these networks, the best route choice is not necessarily a path, but rather a time-adaptive strategy that assigns successors to nodes as a function of time. Nevertheless, in some particular cases an origin-destination path must be chosen a priori, since time-adaptive choices are not allowed. Unfortunately, finding the a priori shortest path is an NP-hard problem. In this paper, we propose a solution method for the a priori shortest path problem, and we show that it can be easily extended to the ranking of the first K shortest paths. Our method exploits the solution of the time-adaptive routing problem as a relaxation of the a priori problem. Computational results are presented showing that, under realistic distributions of travel times and costs, our solution methods are effective and robust. © 2013 Elsevier B.V. All rights reserved.
https://doi.org/10.1016/j.ejor.2013.10.022Cite as:
@article{Nielsen_2014, doi = {10.1016/j.ejor.2013.10.022}, url = {https://doi.org/10.1016%2Fj.ejor.2013.10.022}, year = 2014, month = {aug}, publisher = {Elsevier {BV}}, volume = {236}, number = {3}, pages = {903--914}, author = {Lars Relund Nielsen and Kim Allan Andersen and Daniele Pretolani}, title = {Ranking paths in stochastic time-dependent networks}, journal = {European Journal of Operational Research} }