2015
Autores
Teixeira, D; Cruz, A; Bráz, S; Moreira, A; Relvas, J; Camacho, R;
Publicação
Proceedings of the 30th Annual ACM Symposium on Applied Computing
Abstract
2015
Autores
de Sousa, JF; Mendes Moreira, J;
Publicação
2015 IEEE 18TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS
Abstract
In this paper we briefly present our feelings about urban logistic and its role in urban mobility. In some way, we can say that this is a position paper based on an extensive review of all known related published material. We support the development of new approaches for the management of passenger and freight transport together as a single logistics system; based on the access to more and more sophisticated flows of data and better communication means, we envisage the dissemination of sufficient information for the correct decision of every citizens between several mobility options in real time (especially with the support of mobile technology); and we sustain that new tools are needed to help the design of innovative business models and policies, and the change of habits and behaviors. We visualize urban logistics as a multi-stakeholder, multi-criteria and multimodal mobility dynamic system.
2015
Autores
Mendes Moreira, J; Moreira Matias, L;
Publicação
CEUR Workshop Proceedings
Abstract
2015
Autores
Usó, AM; Moreira, JM; Matias, LM; Kull, M; Lachiche, N;
Publicação
DC@ECML/PKDD
Abstract
2015
Autores
Matias, LM; Ferreira, M; Moreira, JM;
Publicação
Abstract
2015
Autores
Monteiro, MSR; Fontes, DBMM; Fontes, FACC;
Publicação
OPTIMIZATION LETTERS
Abstract
In this work we address the Hop-Constrained Minimum cost Flow Spanning Tree (HMFST) problem with nonlinear costs. The HMFST problem is an extension of the Hop-Constrained Minimum Spanning Tree problem since it considers flow requirements other than unit flows. We propose a hybrid heuristic, based on ant colony optimization and on local search, to solve this class of problems given its combinatorial nature and also that the total costs are nonlinearly flow dependent with a fixed-charge component. We solve a set of benchmark problems available online and compare the results obtained with the ones reported in the literature for a Multi-Population hybrid biased random key Genetic Algorithm (MPGA). Our algorithm proved to be able to find an optimum solution in more than 75 % of the runs, for each problem instance solved, and was also able to improve on many results reported for the MPGA. Furthermore, for every single problem instance we were able to find a feasible solution, which was not the case for the MPGA. Regarding running times, our algorithm improves upon the computational time used by CPLEX and was always lower than that of the MPGA.
The access to the final selection minute is only available to applicants.
Please check the confirmation e-mail of your application to obtain the access code.