On Computing Pareto Optimal Paths in Weighted Time-Dependent Networks - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Information Processing Letters Year : 2021

On Computing Pareto Optimal Paths in Weighted Time-Dependent Networks


A weighted point-availability time-dependent network is a list of temporal edges, where each temporal edge has an appearing time value, a travel time value, and a cost value. In this paper we consider the single source Pareto problem in weighted point-availability time-dependent networks, which consists of computing, for any destination d, all Pareto optimal pairs (t, c), where t and c are the arrival time and the cost of a path from s to d, respectively (a pair (t, c) is Pareto optimal if there is no path with arrival time smaller than t and cost no worse than c or arrival time no greater than t and better cost). We design and analyse a general algorithm for solving this problem, whose time complexity is O(M log P), where M is the number of temporal edges and P is the maximum number of Pareto optimal pairs for each node of the network. This complexity significantly improves the time complexity of the previously known solution. Our algorithm can be used to solve several different minimum cost path problems in weighted point-availability time-dependent networks with a vast variety of cost definitions, and it can be easily modified in order to deal with the single destination Pareto problem. All our results apply to directed networks, but they can be easily adapted to undirected networks with no edges with zero travel time.
Fichier principal
Vignette du fichier
main.pdf (213.07 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03095828 , version 1 (04-01-2021)



Filippo Brunelli, Pierluigi Crescenzi, Laurent Viennot. On Computing Pareto Optimal Paths in Weighted Time-Dependent Networks. Information Processing Letters, In press, ⟨10.1016/j.ipl.2020.106086⟩. ⟨hal-03095828⟩
85 View
182 Download



Gmail Facebook X LinkedIn More