### 2017

Cintrano, Christian; Chicano, Francisco; Alba, Enrique

Robust Bi-objective Shortest Path Problem in Real Road Networks Incollection

In: International Conference on Smart Cities, Smart-CT 2017, pp. 128–136, Springer, Cham, 2017, ISBN: 978-3-319-59513-9.

Abstract | Links | BibTeX | Tags: Bi-objective shortest path, Multi-objective optimization, Robustness, Traffic road network

@incollection{Cintrano2017,

title = {Robust Bi-objective Shortest Path Problem in Real Road Networks},

author = {Christian Cintrano and Francisco Chicano and Enrique Alba},

doi = {10.1007/978-3-319-59513-9_13},

isbn = {978-3-319-59513-9},

year = {2017},

date = {2017-06-01},

booktitle = {International Conference on Smart Cities, Smart-CT 2017},

pages = {128--136},

publisher = {Springer, Cham},

edition = {Lecture No},

abstract = {Road journeys are one of our most frequent daily tasks. Despite we need them, these trips have some associated costs: time, money, pollution, etc. One of the usual ways of modeling the road network is as a graph. The shortest path problem consists in finding the path in a graph that minimizes a certain cost function. However, in real world applications, more than one objective must be optimized simultaneously (e.g. time and pollution) and the data used in the optimization is not precise: it contains errors. In this paper we propose a new mathematical model for the robust bi-objective shortest path problem. In addition, some empirical studies are included to illustrate the utility of our formulation.},

keywords = {Bi-objective shortest path, Multi-objective optimization, Robustness, Traffic road network},

pubstate = {published},

tppubtype = {incollection}

}

Road journeys are one of our most frequent daily tasks. Despite we need them, these trips have some associated costs: time, money, pollution, etc. One of the usual ways of modeling the road network is as a graph. The shortest path problem consists in finding the path in a graph that minimizes a certain cost function. However, in real world applications, more than one objective must be optimized simultaneously (e.g. time and pollution) and the data used in the optimization is not precise: it contains errors. In this paper we propose a new mathematical model for the robust bi-objective shortest path problem. In addition, some empirical studies are included to illustrate the utility of our formulation.