Por favor, use este identificador para citar o enlazar este ítem: http://dspace.azti.es/handle/24689/1850
Ficheros en este ítem:
No hay ficheros asociados a este ítem.
Título : A GRASP-based multi-objective approach for the tuna purse seine fishing fleet routing problem
Autor : Granado, Igor; Silva, Elsa; Carravilla, Maria Antonia; Oliveira, Jose Fernando; Hernando, Leticia; Fernandes, Jose A.
Resumen : Nowadays, the world's fishing fleet uses 20\% more fuel to catch the same amount offish compared to 30 years ago. Addressing this negative environmental and economic performance is crucial due to stricter emission regulations, rising fuel costs, and predicted declines in fish biomass and body sizes due to climate change. Investment in more efficient engines, larger ships and better fuel has been the main response, but this is only feasible in the long term at high infrastructure cost. An alternative is to optimize operations such as the routing of a fleet, which is an extremely complex problem due to its dynamic (time-dependent) moving target characteristics. To date, no other scientific work has approached this problem in its full complexity, i.e., as a dynamic vehicle routing problem with multiple time windows and moving targets. In this paper, two bi-objective mixed linear integer programming (MIP) models are presented, one for the static variant and another for the time-dependent variant. The bi-objective approaches allow to trade off the economic (e.g., probability of high catches) and environmental (e.g., fuel consumption) objectives. To overcome the limitations of exact solutions of the MIP models, a greedy randomized adaptive search procedure for the multi-objective problem (MO-GRASP) is proposed. The computational experiments demonstrate the good performance of the MO-GRASP algorithm with clearly different results when the importance of each objective is varied. In addition, computational experiments conducted on historical data prove the feasibility of applying the MO-GRASP algorithm in a real context and explore the benefits of joint planning (collaborative approach) compared to a non-collaborative strategy. Collaborative approaches enable the definition of better routes that may select slightly worse fishing and planting areas (2.9\%), but in exchange fora significant reduction in fuel consumption (17.3\%) and time at sea (10.1\%) compared to non-collaborative strategies. The final experiment examines the importance of the collaborative approach when the number of available drifting fishing aggregation devices (dFADs) per vessel is reduced.
Palabras clave : Decision support system; Combinatorial optimization; Multi-objective optimization; Fishing fleet planning; Fishing management; ALGORITHM
Fecha de publicación : 2025
Editorial : PERGAMON-ELSEVIER SCIENCE LTD
Tipo de documento: Article
Idioma: 
DOI: 10.1016/j.cor.2024.106891
URI : http://dspace.azti.es/handle/24689/1850
ISSN : 0305-0548
E-ISSN: 1873-765X
Patrocinador: Horizon 2020 research and innovation programme [869353]
National Funds through the Portuguese funding agency [LA/P/0063/2020, UIDB/00319/2020]
Basque Government [IT1504-22]
Spanish Min-istry of Economy and Competitiveness MINECO [PID2023-149195NB-I00]
Basque Research and Technology Alliance (BRTA)
Aparece en las tipos de publicación: Artículos científicos



Los ítems de DSpace están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.