{"id":{"repo_id":"minho-thes","oai_identifier":"oai:repositorium.uminho.pt:1822/97938"},"canonical_url":"https://search.dev.ndltd.org/etd/minho-thes/oai:repositorium.uminho.pt:1822/97938","repository":{"repo_id":"minho-thes","name":"Universidade do Minho","base_url":"http://repositorium.sdum.uminho.pt/oai/request"},"display":{"title":"Minimizing the impact of forest fires","abstract":"Forest fires are a growing environmental, social and economic threat, especially in regions with high vegetation density and frequent ignition occurrences. This thesis addresses the problem of optimizing the allocation of firefighting resources to minimize the impact of forest fires, focusing on scenarios with multiple simultaneous ignitions and limited resources. The research proposes and evaluates several approaches, including single-objective, lexicographic, and multi-objective optimization formulations, to support strategic decision-making in fire suppression. First, a constructive heuristic is developed to quickly generate feasible initial solutions to the firefighting resource allocation and routing problem. Then, metaheuristics, such as the genetic algorithm and differential evolution algorithm are implemented to explore complex and large-scale solution spaces effectively. The proposed methods are validated using a case-study from the Braga district, Portugal, a region characterized by recurrent and severe forest fires. Several operational scenarios are studied, considering different levels of resource availability. The models aim to minimize the number of fires extinguished after 90 minutes, the total burned area, the time required to extinguish all fires, the total used water and the number of firefighting resources needed to extinguish all fires. The results show that metaheuristic-based approaches are efficient in terms of solution quality and adaptability under resource-limited conditions. In particular, the multi-objective solutions allows the decision-maker to identify the compromise solutions and choose according to his/her preferences. This thesis contributes to the development of advanced optimization models for forest fire suppression and presents a computational implementation that can serve as a decision support tool for civil protection entities. The findings highlight the importance of integrating smart resource allocation strategies into operational response plans to increase resilience and reduce the impacts of forest fires, especially in the face of increasing challenges posed by climate change.","abstract_html":"Forest fires are a growing environmental, social and economic threat, especially in regions with high vegetation density and frequent ignition occurrences. This thesis addresses the problem of optimizing the allocation of firefighting resources to minimize the impact of forest fires, focusing on scenarios with multiple simultaneous ignitions and limited resources. The research proposes and evaluates several approaches, including single-objective, lexicographic, and multi-objective optimization formulations, to support strategic decision-making in fire suppression. First, a constructive heuristic is developed to quickly generate feasible initial solutions to the firefighting resource allocation and routing problem. Then, metaheuristics, such as the genetic algorithm and differential evolution algorithm are implemented to explore complex and large-scale solution spaces effectively. The proposed methods are validated using a case-study from the Braga district, Portugal, a region characterized by recurrent and severe forest fires. Several operational scenarios are studied, considering different levels of resource availability. The models aim to minimize the number of fires extinguished after 90 minutes, the total burned area, the time required to extinguish all fires, the total used water and the number of firefighting resources needed to extinguish all fires. The results show that metaheuristic-based approaches are efficient in terms of solution quality and adaptability under resource-limited conditions. In particular, the multi-objective solutions allows the decision-maker to identify the compromise solutions and choose according to his/her preferences. This thesis contributes to the development of advanced optimization models for forest fire suppression and presents a computational implementation that can serve as a decision support tool for civil protection entities. The findings highlight the importance of integrating smart resource allocation strategies into operational response plans to increase resilience and reduce the impacts of forest fires, especially in the face of increasing challenges posed by climate change.","abstract_has_math":false,"creators":["Matos, Marina A."],"institution":"Universidade do Minho","degree_name":"Programa doutoral em Industrial and Systems Engineering","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Rocha, Ana Maria A. C.","Alvelos, Filipe Pereira e"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-07-21","date_published":"2025-07-21","updated_at":"2026-08-21T16:46:39Z","subjects":["Forest fire suppression","Optimization","Heuristics","Metaheuristics","Resource allocation","Supressão de incêndios florestais","Otimização","Heurísticas","Metaheurísticas","Alocação de recursos"],"languages":["eng"],"rights":["openAccess"],"rights_urls":["http://creativecommons.org/licenses/by-nc-sa/4.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1822/97938","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"source_record":{"url":"http://repositorium.sdum.uminho.pt/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Arepositorium.uminho.pt%3A1822%2F97938","prefix":"dim"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Rocha, Ana Maria A. 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This thesis addresses the problem of optimizing the allocation of firefighting resources to minimize the impact of forest fires, focusing on scenarios with multiple simultaneous ignitions and limited resources. The research proposes and evaluates several approaches, including single-objective, lexicographic, and multi-objective optimization formulations, to support strategic decision-making in fire suppression. First, a constructive heuristic is developed to quickly generate feasible initial solutions to the firefighting resource allocation and routing problem. Then, metaheuristics, such as the genetic algorithm and differential evolution algorithm are implemented to explore complex and large-scale solution spaces effectively. The proposed methods are validated using a case-study from the Braga district, Portugal, a region characterized by recurrent and severe forest fires. Several operational scenarios are studied, considering different levels of resource availability. The models aim to minimize the number of fires extinguished after 90 minutes, the total burned area, the time required to extinguish all fires, the total used water and the number of firefighting resources needed to extinguish all fires. The results show that metaheuristic-based approaches are efficient in terms of solution quality and adaptability under resource-limited conditions. In particular, the multi-objective solutions allows the decision-maker to identify the compromise solutions and choose according to his/her preferences. This thesis contributes to the development of advanced optimization models for forest fire suppression and presents a computational implementation that can serve as a decision support tool for civil protection entities. The findings highlight the importance of integrating smart resource allocation strategies into operational response plans to increase resilience and reduce the impacts of forest fires, especially in the face of increasing challenges posed by climate change.","Os incêndios florestais são uma ameaça ambiental, social e económica crescente, especialmente em regiões com elevada densidade de vegetação e ocorrências frequentes de ignição. Esta tese aborda o problema da otimização da alocação de recursos de combate a incêndios para minimizar o impacto dos incêndios florestais, com foco em cenários com múltiplas ignições simultâneas e recursos limitados. A investigação propõe e avalia diversas abordagens, incluindo formulações de otimização de objetivo único, lexicográfica e multiobjetivo, para apoiar a tomada de decisões estratégicas na supressão de incêndios. Em primeiro lugar, é desenvolvida uma heurística construtiva para gerar rapidamente soluções iniciais viáveis para o problema de alocação e roteamento de recursos de combate a incêndios. De seguida, são implementadas metaheurísticas, como o algoritmo genético e o algoritmo de evolução diferencial, para explorar espaços de soluções complexos e de grande escala de forma eficaz. Os métodos propostos são validados utilizando dados reais do distrito de Braga, Portugal, uma região caracterizada por incêndios florestais recorrentes e graves. São estudados vários cenários operacionais, considerando diferentes níveis de disponibilidade de recursos. Os modelos visam minimizar o número de incêndios extintos após 90 minutos, a área total ardida, o tempo necessário para extinguir todos os incêndios, o total de água utilizada e o número de recursos de combate a incêndios necessários para extinguir todos os incêndios. Os resultados mostram que as abordagens baseadas em metaheurísticas são eficientes em termos de qualidade da solução e adaptabilidade em condições de recursos limitados. Em particular, as soluções multi-objectivo permitem ao decisor identificar as soluções de compromisso e escolher de acordo com as suas preferências. Esta tese contribui para o desenvolvimento de modelos avançados de otimização para a supressão de incêndios florestais cuja implementação computacional pode servir de ferramenta de apoio à decisão para entidades de proteção civil. As conclusões realçam a importância de integrar estratégias inteligentes de alocação de recursos nos planos de resposta operacional para aumentar a resiliência e reduzir os impactos dos incêndios florestais, especialmente face aos crescentes desafios impostos pelas alterações climáticas."]},{"key":"dc:title","label":"Title","values":["Minimizing the impact of forest fires"]}]}],"canonical_facts":{"dc:contributor.advisor":["Rocha, Ana Maria A. C.","Alvelos, Filipe Pereira e"],"dc:creator":["Matos, Marina A."],"dc:date.accessioned":["2025-10-31T14:09:28Z"],"dc:date.issued":["2025-07-21"],"dc:description.abstract":["Forest fires are a growing environmental, social and economic threat, especially in regions with high vegetation density and frequent ignition occurrences. This thesis addresses the problem of optimizing the allocation of firefighting resources to minimize the impact of forest fires, focusing on scenarios with multiple simultaneous ignitions and limited resources. The research proposes and evaluates several approaches, including single-objective, lexicographic, and multi-objective optimization formulations, to support strategic decision-making in fire suppression. First, a constructive heuristic is developed to quickly generate feasible initial solutions to the firefighting resource allocation and routing problem. Then, metaheuristics, such as the genetic algorithm and differential evolution algorithm are implemented to explore complex and large-scale solution spaces effectively. The proposed methods are validated using a case-study from the Braga district, Portugal, a region characterized by recurrent and severe forest fires. Several operational scenarios are studied, considering different levels of resource availability. The models aim to minimize the number of fires extinguished after 90 minutes, the total burned area, the time required to extinguish all fires, the total used water and the number of firefighting resources needed to extinguish all fires. The results show that metaheuristic-based approaches are efficient in terms of solution quality and adaptability under resource-limited conditions. In particular, the multi-objective solutions allows the decision-maker to identify the compromise solutions and choose according to his/her preferences. This thesis contributes to the development of advanced optimization models for forest fire suppression and presents a computational implementation that can serve as a decision support tool for civil protection entities. The findings highlight the importance of integrating smart resource allocation strategies into operational response plans to increase resilience and reduce the impacts of forest fires, especially in the face of increasing challenges posed by climate change.","Os incêndios florestais são uma ameaça ambiental, social e económica crescente, especialmente em regiões com elevada densidade de vegetação e ocorrências frequentes de ignição. Esta tese aborda o problema da otimização da alocação de recursos de combate a incêndios para minimizar o impacto dos incêndios florestais, com foco em cenários com múltiplas ignições simultâneas e recursos limitados. A investigação propõe e avalia diversas abordagens, incluindo formulações de otimização de objetivo único, lexicográfica e multiobjetivo, para apoiar a tomada de decisões estratégicas na supressão de incêndios. Em primeiro lugar, é desenvolvida uma heurística construtiva para gerar rapidamente soluções iniciais viáveis para o problema de alocação e roteamento de recursos de combate a incêndios. De seguida, são implementadas metaheurísticas, como o algoritmo genético e o algoritmo de evolução diferencial, para explorar espaços de soluções complexos e de grande escala de forma eficaz. Os métodos propostos são validados utilizando dados reais do distrito de Braga, Portugal, uma região caracterizada por incêndios florestais recorrentes e graves. São estudados vários cenários operacionais, considerando diferentes níveis de disponibilidade de recursos. Os modelos visam minimizar o número de incêndios extintos após 90 minutos, a área total ardida, o tempo necessário para extinguir todos os incêndios, o total de água utilizada e o número de recursos de combate a incêndios necessários para extinguir todos os incêndios. Os resultados mostram que as abordagens baseadas em metaheurísticas são eficientes em termos de qualidade da solução e adaptabilidade em condições de recursos limitados. Em particular, as soluções multi-objectivo permitem ao decisor identificar as soluções de compromisso e escolher de acordo com as suas preferências. Esta tese contribui para o desenvolvimento de modelos avançados de otimização para a supressão de incêndios florestais cuja implementação computacional pode servir de ferramenta de apoio à decisão para entidades de proteção civil. As conclusões realçam a importância de integrar estratégias inteligentes de alocação de recursos nos planos de resposta operacional para aumentar a resiliência e reduzir os impactos dos incêndios florestais, especialmente face aos crescentes desafios impostos pelas alterações climáticas."],"dc:identifier.uri":["https://hdl.handle.net/1822/97938"],"dc:language.iso":["eng"],"dc:relation":["Minimizing the impact of Forest Fires [UI/BD/150936/2021]","An Optimization Framework to reduce Forest Fire [PCIF/GRF/0141/2019]","PCIF/GRF/0141/2019"],"dc:rights":["openAccess"],"dc:rights.uri":["http://creativecommons.org/licenses/by-nc-sa/4.0/"],"dc:subject":["Forest fire suppression","Optimization","Heuristics","Metaheuristics","Resource allocation","Supressão de incêndios florestais","Otimização","Heurísticas","Metaheurísticas","Alocação de recursos"],"dc:title":["Minimizing the impact of forest fires"],"dc:type":["doctoralThesis"],"thesis:degree_name":["Programa doutoral em Industrial and Systems Engineering"],"thesis:institution_name":["Universidade do Minho"]},"updated_at":"2026-08-21T16:46:39Z"}