Capítulo de Libro
A Mathematical optimization and machine learning approach for pipeline routing
Autor/es | Hinojosa Bergillos, Yolanda
Ponce López, Diego |
Coordinador/Director | Guadix Martín, José
Lilic, Milica Rosales Martínez, Marina |
Departamento | Universidad de Sevilla. Departamento de Economía Aplicada I Universidad de Sevilla. Departamento de Estadística e Investigación Operativa |
Fecha de publicación | 2022 |
Fecha de depósito | 2024-02-07 |
Publicado en |
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ISBN/ISSN | 9781003276609 |
Resumen | In shipbuilding, pipeline routing is a difficult problem as space is rather limited. This constraint and others related to obstacles, costs, legislation, or operability are considered to set the pipeline layout by means ... In shipbuilding, pipeline routing is a difficult problem as space is rather limited. This constraint and others related to obstacles, costs, legislation, or operability are considered to set the pipeline layout by means of a mathematical model. The problem is solved in an exact or heuristic way when the complexity increases when dealing with real instances, using Artificial Intelligence tools. In this work, we outline some issues and solution proposals which have been applied to a case study that shows the usefulness of mathematical tools in design engineering. |
Asociado a la publicación | Guadix Martín, J., Lilic, M., y Rosales Martínez, M. (2022). AI Knowledge Transfer from the University to Society: Applications in High-Impact Sectors. Estados Unidos: Taylor and Francis.https://idus.us.es/handle/11441/133714 |
Cita | Hinojosa Bergillos, Y. y Ponce López, D. (2022). A Mathematical optimization and machine learning approach for pipeline routing. En J. Guadix Martín, M. Lilic, M. Rosales Martínez (Ed.), AI Knowledge Transfer from the University to Society: Applications in High-Impact Sectors (pp. 65-69). Boca Raton: Taylor and Francis. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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Mathematical_optimization.pdf | 110.2Kb | [PDF] | Ver/ | |