A Multidimensional Approach for Logistics Routing in the Smart Territory

• Everton Cavalcante Universidade Federal do Rio Grande do Norte http://orcid.org/0000-0002-2475-5075
• Thais Batista Universidade Federal do Rio Grande do Norte https://orcid.org/0000-0003-3558-1450
• Marcel Oliveira Universidade Federal do Rio Grande do Norte https://orcid.org/0000-0002-3023-2748
• Jorge Pereira Universidade Federal do Rio Grande do Norte
• Victor Ribeiro Universidade Federal do Rio Grande do Norte
• Matthieu Oliveira Universidade Federal do Rio Grande do Norte

DOI: https://doi.org/10.5753/sbsi_estendido.2022.222988

Resumo

This work proposes a multidimensional approach for analyzing the routing problem to determine the best routes considering data related to different domains of a city. The proposed strategy defines (i) a quality function for each considered dimension to evaluate the route quality and (ii) a utility function that simultaneously considers the different dimensions by weighting each of them at the decision maker's choice. The approach was implemented on a georeferenced smart city platform that integrates data from several city domains. As proof of concept, the platform is used to combine routing and public safety data and indicates the best routes according to these criteria.

Palavras-chave: Logistics, Routing, Smart city platform

Referências

Abella, A., Ortiz-de-Urbina-Criado, M., De-Pablos-Heredero, C. (2017). A model for the analysis of data-driven innovation and value generation in smart cities’ ecosystems. Cities, 64:47–53. DOI: 10.1016/j.cities.2017.01.011.

Ehrgott, M. (2005). Multicriteria optimization. Springer Berlin Heidelberg, Germany, 2 edition. DOI: 10.1007/3-540-27659-9.

Ehrgott, M. Gandibleux, X. (2003). Multiobjective combinatorial optimization – theory, methodology, and applications. In Ehrgott, M. Gandibleux, X., editors, Multiple criteria optimization: State of the art annotated bibliography surveys, volume 52 of International Series in Operations Research & Management Science, pages 369–444. Kluwer Academic Publishers/Springer Boston, USA. DOI: 10.1007/0-306-48107-3_8.

Giovanella, C. (2014). Smart territory analytics: Toward a shared vision. In Proceedings of 47th Scientific Meeting of the Italian Statistical Society, Italy. CUEC.

Internet Engineering Task Force (2016). RFC 7946 - The GeoJSON Format. Available at: https://tools.ietf.org/html/rfc7946.

Korczak, J. Kijewska, K. (2019). Smart logistics in the development of smart cities. Transportation Research Procedia, 39:201–2011. DOI: 10.1016/j.trpro.2019.06.022.

Kubek, D. Wiecek, P. (2019). An integrated multi-layer decision-making framework in the Physical Internet concept for the city logistics. Transportation Research Procedia, 39:221–230. DOI: 10.1016/j.trpro.2019.06.024.

Luxen, D. Vetter, C. (2011). Real-time routing with OpenStreetMap data. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 513–516, USA. ACM. DOI: 10.1145/2093973.2094062.

Pereira, J., Batista, T., Cavalcante, E., Souza, A., Lopes, F., Cacho, N. (2022). A platform for integrating heterogeneous data and developing smart city applications. Future Generation Computer Systems, 128:552–566. DOI: 10.1016/j.future.2021.10.030.

Santana, E. F. Z., Chaves, A. P., Gerosa, M. A., Kon, F., Milojicic, D. S. (2017). Software platforms for smart cities: Concepts, requirements, challenges, and a unified reference architecture. ACM Computing Surveys, 50(6). DOI: 10.1145/3124391.

Taniguchi, E., Thompson, R. G., Yamada, T., van Duin, R. (2001). City logistics: Network modeling and intelligent transport systems. Emerald Publishing, United Kingdom. DOI: 10.1108/9780585473840.