Publication
Generation and optimization of inspection routes for economic and food safety
| dc.contributor.author | Barros, Telmo | |
| dc.contributor.author | Oliveira, Alexandra | |
| dc.contributor.author | Cardoso, Henrique Lopes | |
| dc.contributor.author | Reis, Luís Paulo | |
| dc.contributor.author | Caldeira, Cristina | |
| dc.contributor.author | Machado, João Pedro | |
| dc.contributor.author | Oliveira, Alexandra | |
| dc.date.accessioned | 2025-10-28T14:41:33Z | |
| dc.date.available | 2025-10-28T14:41:33Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Artificial intelligence techniques have been applied to diverse business and governmental areas, in order to take advantage of the huge amount of information that is generated within specific organizations or institutions. Business intelligence can be seen as the process of converting such information into actionable knowledge, which is the basis for data-driven decision making. With this in mind, this work is framed in a project that seeks to improve the activity of the Portuguese Food and Economic Safety Authority, regarding prevention in the areas of food safety and economic enforcement. More specifically, this paper focuses on the generation and optimization of flexible inspection routes. An optimal inspection route seeks to maximize the number of targeted Economic Operators, or the utility gained from the set of Economic Operators that are actually inspected. For that, each Economic Operator is assigned an inspection utility value. The problem was then modelled as a Multi-Depot Periodic Vehicle Routing Problem with Time Windows, and solved using both exact and meta-heuristic methods. The comparison of the meta-heuristic algorithms showed a versatile Hill Climbing implementation in different test cases that explored the effect of the Economic Operators dispersion and density. | por |
| dc.identifier.citation | Barros, T., Oliveira, A., Cardoso, H. L., Reis, L. P., Caldeira, C., & Machado, J. P. (2025). Generation and optimization of inspection routes for economic and food safety. 12th International Conference on Agents and Artificial Intelligence, 268–278. https://doi.org/10.5220/0009182002680278 | |
| dc.identifier.doi | 10.5220/0009182002680278 | |
| dc.identifier.isbn | 978-989-758-395-7 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/30688 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | SCITEPRESS–Science and Technology Publications | |
| dc.relation | LIACC (FCT/UID/CEC/0027/2020) | |
| dc.relation.hasversion | https://www.scitepress.org/Papers/2020/91820/91820.pdf | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Planning | |
| dc.subject | Scheduling | |
| dc.subject | Optimization | |
| dc.subject | Decision support | |
| dc.subject | Vehicle routing problem | |
| dc.title | Generation and optimization of inspection routes for economic and food safety | por |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.citation.conferenceDate | 2020 | |
| oaire.citation.conferencePlace | Valletta, Malta | |
| oaire.citation.endPage | 278 | |
| oaire.citation.startPage | 268 | |
| oaire.citation.title | Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020)- Volume 2 | |
| oaire.citation.volume | 2 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Oliveira | |
| person.givenName | Alexandra | |
| person.identifier.ciencia-id | 161A-55D9-C256 | |
| person.identifier.orcid | 0000-0001-5872-5504 | |
| person.identifier.scopus-author-id | 56340903500 | |
| relation.isAuthorOfPublication | d6f940a1-3dba-41d2-9a5e-dc1f313eec07 | |
| relation.isAuthorOfPublication.latestForDiscovery | d6f940a1-3dba-41d2-9a5e-dc1f313eec07 |