Isabel Martins
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Professora Auxiliar
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About
Isabel Martins is an Auxiliar Professor at the Universidade de Lisboa Instituto Superior de Agronomia in Portugal. Her research focuses on forest management, optimization problems related to harvest scheduling, and the simulation of fire effects on forest systems. Recent publications include studies on heuristic approaches for harvest scheduling and the application of Monte Carlo tree search methods in forestry.
Recent Grants
Grant: Close
An Optimization Framework to Reduce Forest Fire
Open Date: 2021-03-01
Close Date: 2024-02-01
Grant: Close
FIRE-RES H2020
Open Date: 2021-01-01
Close Date: 2025-01-01
Grant: Close
Novos modelos de negócio e mecanismos para a oferta sustentada e pagamento de serviços de ecossistemas florestais
Open Date: 2019-02-01
Close Date: 2022-10-31
Grant: Close
Métodos de decisão em gestão de ecossistemas florestais: uma aproximação bioeconómica integrada para a sustentabilidade
Open Date: 2018-08-16
Close Date: 2022-01-31
Grant: Close
Métodos de decisão em gestão de ecossistemas florestais: uma aproximação bioeconómica para a sustentabilidade
Open Date: 2018-01-01
Close Date: 2021-01-01
Articles (4)
Year:
2025
Year:
2025
This study aims at solving a harvesting scheduling optimization problem with constraints on the clearcut area with additional constraints on clearcut proximity. The objective function is defined as the net present value generated by harvesting discounted by a penalty for each clearcut. This problem arises to reduce the negative environmental impact of excessive harvesting. We propose the connected‐bucket model, the so‐called bucket model with additional constraints on bucket connectivity and two definitions of stand adjacency, and a Dantzig–Wolfe decomposition. The decomposed model is solved by branch‐and‐price and the connected‐bucket model by a general‐purpose mixed integer programming solver (CPLEX). We compare the quality of the solutions obtained with both approaches for real instances. The branch‐and‐price approach found better solutions for the majority of the instances.
Year:
2022
Year:
2020
Collaborators (4)
João Pedro Pedroso
Associate professor
University of Porto
Robert Marušák
-
Adelaide Cerveira
-
Susete Marques
Instituto Superior de Agronomia
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