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12 results on '"Godinho, María Teresa"'

1. Finding $K$ dissimilar paths using integer linear formulations

Author

Moghanni, Ali, Pascoal, Marta, and Godinho, Maria Teresa

Subjects
Mathematics - Optimization and Control
Abstract

While finding a path between two nodes is the basis for several applications, the need for alternative paths also may have various motivations. For instance, this can be of interest for ensuring reliability in a telecommunications network, for reducing the consequences of possible accidents in the transportation of hazardous materials, or to decrease the risk of robberies in money distribution. Each of these applications has particular characteristics, but they all have the common purpose of searching for a set of paths which are as dissimilar as possible with respect to the nodes/arcs that compose them. In this work we present linear integer programming formulations for finding $K$ dissimilar paths, with the main goal of preventing the overlap of arcs in the paths for a given integer $K$. The different formulations are tested for randomly generated general networks and for grid networks. The obtained results are compared in terms of the solutions' dissimilarity and of the run time. Two of the new formulations are able to find 10 paths with better average and minimum dissimilarity values than an iterative approach in the literature, in less than 20 seconds, for random networks with 500 nodes and 5000 arcs.

Published
2021

3. Towards a Unified Framework for Routing and Scheduling Planning in an Integrated Continuous Care Unit

Author

Godinho, Maria Teresa, Lopes, Maria João, Vigo, Daniele, Editor-in-Chief, Agnetis, Alessandro, Series Editor, Amaldi, Edoardo, Series Editor, Guerriero, Francesca, Series Editor, Lucidi, Stefano, Series Editor, Messina, Enza, Series Editor, Sforza, Antonio, Series Editor, Aringhieri, Roberto, editor, Maggioni, Francesca, editor, Lanzarone, Ettore, editor, Reuter-Oppermann, Melanie, editor, Righini, Giovanni, editor, and Vespucci, Maria Teresa, editor

Published
2023
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4. New Models for Finding K Short and Dissimilar Paths

Author

Pascoal, Marta, Godinho, Maria Teresa, Moghanni, Ali, Almeida, João Paulo, editor, Geraldes, Carla Soares, editor, Lopes, Isabel Cristina, editor, Moniz, Samuel, editor, Oliveira, José Fernando, editor, and Pinto, Alberto Adrego, editor

Published
2023
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7. Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering

Author

Melgar-García, Laura, Godinho, Maria Teresa, Espada, Rita, Gutiérrez-Avilés, David, Brito, Isabel Sofia, Martínez-Álvarez, Francisco, Troncoso, Alicia, Rubio-Escudero, Cristina, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Herrero, Álvaro, editor, Cambra, Carlos, editor, Urda, Daniel, editor, Sedano, Javier, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2021
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9. A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture

Author

Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos, Universidad de Sevilla. TIC-254: Data Science and Big Data Lab, Universidad de Sevilla. TIC-134: Sistemas Informáticos, Ministerio de Ciencia e Innovación (MICIN). España, Junta de Andalucía, Fundação para a Ciência e a Tecnologia (FCT), Melgar García, Laura, Gutiérrez Avilés, David, Godinho, María Teresa, Espada, Rita, Brito, Isabel Sofía, Martínez Álvarez, Francisco, Troncoso Lora, Alicia, Rubio Escudero, Cristina, Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos, Universidad de Sevilla. TIC-254: Data Science and Big Data Lab, Universidad de Sevilla. TIC-134: Sistemas Informáticos, Ministerio de Ciencia e Innovación (MICIN). España, Junta de Andalucía, Fundação para a Ciência e a Tecnologia (FCT), Melgar García, Laura, Gutiérrez Avilés, David, Godinho, María Teresa, Espada, Rita, Brito, Isabel Sofía, Martínez Álvarez, Francisco, Troncoso Lora, Alicia, and Rubio Escudero, Cristina

Abstract

Precision agriculture focuses on the development of site-specific harvest considering the variability of each crop area. Vegetation indices allow the study and delineation of different characteristics of each field zone, generally invisible to the naked-eye. This paper introduces a new big data triclustering approach based on evolutionary algorithms. The algorithm shows its capability to discover three-dimensional pat-terns on the basis of vegetation indices from vine crops. Different vegetation indices have been tested to find different patterns in the crops. The results reported using a vineyard crop located in Portugal depicts four areas with different moisture stress particularities that can lead to changes in the management of the vineyard. Furthermore, scalability studies have been performed, showing that the proposed algorithm is suitable for dealing with big datasets.

Published
2022

11. Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering

Author

Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos, Ministerio de Economía y Competitividad (MINECO). España, Fundaçao para a Ciencia e a Tecnología (FCT), Melgar García, Laura, Godinho, María Teresa, Espada, Rita, Gutiérrez Avilés, David, Brito, Isabel Sofía, Martínez Álvarez, Francisco, Troncoso Lora, Alicia, Rubio Escudero, Cristina, Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos, Ministerio de Economía y Competitividad (MINECO). España, Fundaçao para a Ciencia e a Tecnología (FCT), Melgar García, Laura, Godinho, María Teresa, Espada, Rita, Gutiérrez Avilés, David, Brito, Isabel Sofía, Martínez Álvarez, Francisco, Troncoso Lora, Alicia, and Rubio Escudero, Cristina

Abstract

Agriculture has undergone some very important changes over the last few decades. The emergence and evolution of precision agri culture has allowed to move from the uniform site management to the site-specific management, with both economic and environmental advan tages. However, to be implemented effectively, site-specific management requires within-field spatial variability to be well-known and character ized. In this paper, an algorithm that delineates within-field management zones in a maize plantation is introduced. The algorithm, based on tri clustering, mines clusters from temporal remote sensing data. Data from maize crops in Alentejo, Portugal, have been used to assess the suit ability of applying triclustering to discover patterns over time, that may eventually help farmers to improve their harvests.

Published
2020

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