Made available in DSpace on 2014-07-29T15:08:20Z (GMT). No. of bitstreams: 1 dissertacao geraldo valeriano eec.pdf: 1215973 bytes, checksum: 4ceb98a1d5250ad33d16a8997882d277 (MD5) Previous issue date: 2009-06-29 / This work presents two methods to solve the problem of Electric Distribution Networks (EDN) with geographical and power restrictions. The high cost of the project involving EDN together with lack of efficient methods when working with real applications justifies the development of this research. Taking into account concepts of heuristic and metaheuristic two methods are proposed: The first is based on the Hill-Climbing (HC) heuristic and the second is based on the Simulated Annealing (SA) metaheuristic. The possible paths are provided by the Delaunay triangulation and it is considered the natural and socio-political obstacles of the site where you want to locate a new energy network. The dimension of the EDN feeders is calculated using the power flow results from the Forward-Backward method. The initial solution is found using an intelligent method. Then the SA metaheuristic and/or HC heuristic are used providing a good solution for a new EDN in comparison with the heuristic used to find the initial solution. A comparison is also made between the two proposed methods / RESUMO Neste trabalho são apresentados dois métodos para resolver o problema de planejamento de rede de distribuição de energia elétrica (RDEE) com restrições geográficas e elétricas. O custo elevado que envolve o projeto de RDEE unido à escassez de métodos eficientes quando se trata de aplicações reais justificam o desenvolvimento desta pesquisa. Considerando os conceitos de heurística e metaheurística são propostos dois métodos: o primeiro é baseado na heurística Hill-Climbing (HC) e o segundo é baseado na metaheurística Simulated Annealing (SA). Os possíveis caminhos são fornecidos pela triangulação de Delaunay e são considerados os obstáculos naturais e políticosociais (restrições geográficas) do local onde se deseja implantar a nova rede de energia elétrica. O dimensionamento dos alimentadores da RDEE é feito utilizando-se do fluxo de potência calculado pelo método Backward-Forward. A solução inicial é encontrada utilizando-se um método inteligente. A metaheurística SA e/ou a heurística HC são então utilizadas, fornecendo uma boa solução para uma nova RDEE, em relação à heurística utilizada para encontrar a solução inicial. Também é realizada uma comparação entre os dois métodos propostos.

Submitted by Cássia Santos (cassia.bcufg@gmail.com) on 2017-07-03T12:59:51Z No. of bitstreams: 2 Dissertação - Guilherme Resende Deus - 2016.pdf: 3406372 bytes, checksum: aaa431a0fa0dd2323a74cf35fb63f892 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2017-07-10T11:44:22Z (GMT) No. of bitstreams: 2 Dissertação - Guilherme Resende Deus - 2016.pdf: 3406372 bytes, checksum: aaa431a0fa0dd2323a74cf35fb63f892 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-07-10T11:44:22Z (GMT). No. of bitstreams: 2 Dissertação - Guilherme Resende Deus - 2016.pdf: 3406372 bytes, checksum: aaa431a0fa0dd2323a74cf35fb63f892 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-02-02 / The objective of this work is to find reasonable solutions to the problem of optimization of hydrothermal generating systems by means of metaheuristics based on particle swarms. The proposed problem is complex, dynamic, nonlinear and presents some stochastic variables. The study consisted of the implementation of particle swarm algorithms, more specifically the variants of the Particle Swarm Optimization (PSO) algorithm: LSSPSO, ABeePSO and KFPSO. The algorithms were run in a mill simulator containing data from eight National Interconnected System mills during the five year period. The results were compared with the studies using the Nonlinear Programming (NLP) algorithm, and it was concluded that although the presented meta-heuristics were able to obtain a Final Storage Energy value equal to NLP, they did not have a generation cost Equivalent to or less than the Nonlinear Programming method. / O trabalho objetiva encontrar soluções razoáveis para o problema de otimização de sistemas hidrotérmicos de geração por meio de meta-heurísiticas baseadas em enxame de partículas. O problema proposto é complexo, dinâmico, não linear e apresenta algumas variáveis estocásticas. O estudo consistiu na implementação de algoritmos baseados em enxame de partículas, mais especificamente das variantes do algoritmo Particle Swarm Optimization (PSO): LSSPSO, ABeePSO e KFPSO. Os algoritmos foram executados em um simulador de usinas que contém dados de oito usinas do Sistema Interligado Nacional durante o período de cinco anos. Os resultados foram comparados com os estudos que utilizam o algoritmo de Programação Não-Linear (PNL), e conclui-se que apesar de as meta-heurísticas apresentadas conseguirem obter um valor de Energia Armazenada Final igual ao PNL, não obtiveram um custo de geração equivalente ou inferior ao método de Programação Não-Linear.

Submitted by Geandra Rodrigues (geandrar@gmail.com) on 2018-03-27T14:05:45Z No. of bitstreams: 1 franciscocarlosrodriguescoelho.pdf: 4772391 bytes, checksum: e11633134429c05832808dad96be9940 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-03-27T14:28:31Z (GMT) No. of bitstreams: 1 franciscocarlosrodriguescoelho.pdf: 4772391 bytes, checksum: e11633134429c05832808dad96be9940 (MD5) / Made available in DSpace on 2018-03-27T14:28:31Z (GMT). No. of bitstreams: 1 franciscocarlosrodriguescoelho.pdf: 4772391 bytes, checksum: e11633134429c05832808dad96be9940 (MD5) Previous issue date: 2018-02-22 / A alocação de Geração Distribuída (GD) em sistemas de distribuição de energia elétrica consiste em determinar os barramentos para conexão destas unidades geradoras, e o montante de potência a ser injetado, visando um ou mais objetivos, que podem ser: redução das perdas de potência ativa, melhorias no perfil de tensão, minimização dos custos operacionais, maximização da geração de energia, ganhos ambientais, dentre outros. O principal objetivo considerado neste trabalho é a minimização das perdas de potência ativa, mantendo as tensões dos barramentos dentro de limites recomendados. Para alcançar este objetivo, uma metodologia de otimização é proposta, tratando separadamente os problemas de localização das unidades geradoras no sistema, e o dimensionamento destas unidades. A determinação das barras com conexão de GD é realizada através de uma nova técnica de otimização metaheurística, implementada no MATLAB, denominada Metaheurística Empírica Discreta (MED). Já o dimensionamento das unidades de GD é realizado de duas formas distintas, a depender do tipo de sistema de distribuição analisado. No caso dos sistemas cujos dados são equivalentes monofásicos, o montante de potencia é determinado por um Fluxo de Potência Ótimo implementado no software comercial LINGO. A segunda estratégia de determinação da potência despachada é empregada no caso dos testes realizados com sistemas trifásicos desbalanceados, cujo dimensionamento é feito pelo método do gradiente descendente e o cálculo do fluxo de potência é realizado pelo software OpenDSS. Os três sistemas equivalentes monofásicos utilizados são compostos por 33, 69 e 476 barras, enquanto os dois trifásicos desequilibrados possuem 34 e 123 barras. A qualidade da metodologia proposta na resolução do problema de alocação de geração distribuída é avaliada através de comparações com a literatura especializada, comparações com outras metaheurísticas e testes de robustez. Os resultados provenientes de simulações com alocação de três e quatro unidades de GD em sistemas de distribuição de energia elétrica mostram que a metodologia proposta é eficiente, sendo capaz de produzir resultados com significativas reduções nas perdas de potência ativa e perfis de tensão adequados. / The optimal Distributed Generation (DG) allocation problem consists in choosing the best locations of those distributed power plants at the distribution system, and to define its amount of power injection. The approach can be either single or multiobjective. The main objectives are: minimization of total power loss, voltage profile improvement, operational cost minimization, maximization of distributed generation capacity, environmental gains, among others. In this work, the main goal pursued is the total power loss minimization of the distribution system, keeping the buses voltages within the predetermined limits. To achieve this goal, an optimization methodology is proposed. This approach treats separately the location problem and the power dispatched by the generation units. The busbars connected to distributed generation are determined through a new metaheuristic algorithm, implemented in MATLAB, named Empirical Discrete Metaheuristic (EDM). The amount of power injection is solved by an Optimum Power Flow implemented in the commercial software LINGO, or by the Steepest Descent Method in the MATLAB environment. The first strategy to determine the DG dispatch is used on simulations with single phase equivalents systems. The second one is employed in the amount of power determination in unbalanced three phase systems, which the power flow is carried out by the open source software OpenDSS. The three single phase equivalent test systems analyzed are composed by 33, 69 and 476 buses, while the two systems with three phases have 34 and 123 buses, each. To evaluate the proposed methodology quality, comparisons to published works in the specialized literature are made. Also, robustness tests and comparisons to other well succeed metaheuristics are carried out. The results were obtained from simulations with three and four DG units in electric power distribution systems. These results consistently show that the proposed methodology is efficient, providing DGs configurations that significantly reduces the active power losses and keep the voltages at adequate levels.

Cette thèse comporte deux parties majeures : la première partie est dédiée à l'étude du problème sparse CARP déterministe où nous avons développé une transformation du sparse CARP en un sparse CVRP. La seconde est consacrée au problème sparse CARP avec coûts sous incertitude. Nous avons donné une formulation mathématique du problème en min-max. Cette modélisation a permis d'identifier le pire scénario pour le problème robuste. Deux approches algorithmiques ont été proposées pour une résolution approchée. / This dissertation consists of two main parts : in the first part, we study the detreministic capacitated arc routing problem over sparse underlying graphs wher we have developed a new transformation techniquevof sparse CARP into sparse CVRP. The second part is consecrated about the sparse CARP with travel costs uncertainty. We have given a mathematical formulation of the probleme in min-max. A worst scenario for the robust problem is then identified, and two algorithmic approaches are proposed to determine a solution of the studied problem.

Notre travail s’intéresse à un cas très particulier des terminaux à conteneurs, il s’agit des terminaux à conteneurs automatisés, qui en plus des véhicules autoguidés, sont équipés de grues de quai et de grues de stockage automatiques (grues de cour), ce qui pousse souvent les scientifiques à considérer les problèmes d’ordonnancement intégré dans les terminaux automatisés ou semi-automatisés. Nous traitons dans ce travail l’optimisation de plusieurs objectifs pour stocker les conteneurs d'une manière efficace et réaliste. Nous traitons le problème d’ordonnancement intégré considérant les trois équipements d’un terminal à conteneurs automatisé soient: les véhicules autoguidés, les grues de quai et les grues de baie (éventuellement). L’objectif principal de cette étude est la minimisation du coût opérationnel de stockage de conteneurs dans un terminal maritime automatisé / AIn our study, we consider two optimization problems in automated container terminals at import; the first is the vehicle scheduling problem; and the second is the integrated problem of location assignment and vehicle scheduling. In the first part of our study, we propose different traffic layout adapted to the two studied problems and to every kind of automated container terminal. We also introduce relevant reviews of literature treating the optimization of container handling systems at maritime terminal, the optimization of general automated guided vehicle system and the multi-objective optimization in general, and in particular context of maritime container terminals. In the second part, we resolve the planning of QC-AV-ASC (Quay Cranes-Automated Vehicles - Automated Stacking Cranes). We present an effective model for every kind of traffic layout. Moreover, we propose an efficient bi-objective model which is important to determine the optimal storage time and the minimal number of required AVs. CPLEX resolutions are used to prove the efficiency of our modelling approach. In the third part of this thesis, we explore a problem which has not been sufficiently studied: the integrated problem of location assignment and vehicle scheduling (IPLAVS), in Maritime Automated Container Terminal (MACT) at import. This part represents a new and realistic approach of MACT optimization considering mono-objective and multi-objective aspect.

Aujourd'hui, le problème de stationnement devient l'un des enjeux majeurs de la recherche dans la planification des transports urbains et la gestion du trafic. En fait, les conséquences de l'absence de places de stationnement ainsi que la gestion inadéquate de ces installations sont énormes. L'objectif de cette thèse est de fournir des algorithmes efficaces et robustes afin que les conducteurs gagnent du temps et de l'argent et aussi augmenter les revenus des gestionnaires de parking. Le problème est formulé comme un problème d'affectation multi-objectifs dans des environnements statique et dynamique. Tout d'abord, dans l'environnement statique, nous proposons de nouvelles heuristiques en deux phases pour calculer une approximation de l'ensemble des solutions efficaces pour un problème bi-objectif. Dans la première phase, nous générons l'ensemble des solutions supportées par un algorithme dichotomique standard. Dans la deuxième phase, nous proposons quatre métaheuristiques pour générer une approximation des solutions non supportées. Les approches proposées sont testées sur le problème du plus court chemin bi-objectif et le problème d'affectation bi-objectif. Dans le contexte de l'environnement dynamique, nous proposons une formulation du problème sous forme d'un programme linéaire en nombres entiers mixtes qui est résolue à plusieurs reprises sur un horizon de temps donné. Les fonctions objectives considérées, permettent un équilibre entre la satisfaction des conducteurs et l'intérêt du gestionnaire de parking. Deux approches sont proposées pour résoudre ce problème d'affectation dynamique avec ou sans phase d'apprentissage. Pour renforcer la phase d'apprentissage, un algorithme à estimation de distribution est proposé pour prévoir la demande future. Pour évaluer l'efficacité des algorithmes proposés, des essais de simulation ont été effectués. Aussi une mise en œuvre pilote a été menée dans le parking à l'Université de Valenciennes en utilisant une plateforme existante, appelée Context Aware Transportation Services (CATS), qui permet le déploiement dynamique de services. Cette plate-forme peut dynamiquement passer d'une approche à l'autre en fonction du contexte. Enfin cette thèse s'inscrit dans le projet SYstem For Smart Road Applications ( SYFRA). / The parking problem is nowadays one of the major issues in urban transportation planning and traffic management research. In fact, the consequences of the lack of parking slots along with the inadequate management of these facilities are tremendous. The aim of this thesis is to provide efficient and robust algorithms in order to save time and money for drivers and to increase the income of parking managers. The problem is formulated as a multi-objective assignment problem in static and dynamic environments. First, for the static environment, we propose new two-phase heuristics to calculate an approximation of the set of efficient solutions for a bi-objective problem. In the first phase, we generate the supported efficient set with a standard dichotomic algorithm. In the second phase we use four metaheuristics to generate an approximation of the non-supported efficient solutions. The proposed approaches are tested on the bi-objective shortest path problem and the biobjective assignment problem. For the dynamic environment, we propose a mixed integer linear programming formulation that is solved several times over a given horizon. The objective functions consist of a balance between the satisfaction of drivers and the interest of the parking managers. Two approaches are proposed for this dynamic assignment problem with or without learning phase. To reinforce the learning phase, an estimation of distribution algorithm is proposed to predict the future demand. In order to evaluate the effectiveness of the proposed algorithms, simulation tests have been carried out. A pilot implementation has also been conducted in the parking of the University of Valenciennes, using an existing platform called framework for context aware transportation services, which allows dynamic deployment of services. This platform can dynamically switch from one approach to another depending on the context. This thesis is part of the project SYstem For Smart Road Applications (SYFRA).

L’Optimisation Combinatoire (OC) est un domaine de recherche qui est en perpétuel changement. Résoudre un problème d’optimisation combinatoire (POC) consiste essentiellement à trouver la ou les meilleures solutions dans un ensemble des solutions réalisables appelé espace de recherche qui est généralement de cardinalité exponentielle en la taille du problème. Pour résoudre des POC, plusieurs méthodes ont été proposées dans la littérature. On distingue principalement les méthodes exactes et les méthodes d’approximation. Ne pouvant pas viser une résolution exacte de problèmes NP-Complets lorsque la taille du problème dépasse une certain seuil, les chercheurs on eu de plus en plus recours, depuis quelques décennies, aux algorithmes dits hybrides (AH) ou encore à au calcul parallèle. Dans cette thèse, nous considérons la classe POC des problèmes de conception d'un réseau fiable. Nous présentons un algorithme hybride parallèle d'approximation basé sur un algorithme glouton, un algorithme de relaxation Lagrangienne et un algorithme génétique, qui produit des bornes inférieure et supérieure pour les formulations à base de flows. Afin de valider l'approche proposée, une série d'expérimentations est menée sur plusieurs applications: le Problème de conception d'un réseau k-arête-connexe avec contrainte de borne (kHNDP) avec L=2,3, le problème de conception d'un réseau fiable Steiner k-arête-connexe (SkESNDP) et ensuite deux problèmes plus généraux, à savoir le kHNDP avec L >= 2 et le problème de conception d'un réseau fiable k-arête-connexe (kESNDP). L'étude expérimentale de la parallélisation est présentée après cela. Dans la dernière partie de ce travail, nous présentons deux algorithmes parallèles exactes: un Branch-and-Bound distribué et un Branch-and-Cut distribué. Une série d'expérimentation a été menée sur une grappe de 128 processeurs, et des accélération intéressantes ont été atteintes pour la résolution du problèmes kHNDP avec k=3 et L=3. / Combinatorial Optimization (CO) is an area of research that is in a constant progress. Solving a Combinatorial Optimization Problem (COP) consists essentially in finding the best solution (s) in a set of feasible solutions called a search space that is usually exponential in cardinality in the size of the problem. To solve COPs, several methods have been proposed in the literature. A distinction is made mainly between exact methods and approximation methods. Since it is not possible to aim for an exact resolution of NP-Complete problems when the size of the problem exceeds a certain threshold, researchers have increasingly used Hybrid (HA) or parallel computing algorithms in recent decades. In this thesis we consider the COP class of Survivability Network Design Problems. We present an approximation parallel hybrid algorithm based on a greedy algorithm, a Lagrangian relaxation algorithm and a genetic algorithm which produces both lower and upper bounds for flow-based formulations. In order to validate the proposed approach, a series of experiments is carried out on several applications: the k-Edge-Connected Hop-Constrained Network Design Problem (kHNDP) when L = 2,3, The problem of the Steiner k-Edge-Connected Network Design Problem (SkESNDP) and then, two more general problems namely the kHNDP when L >= 2 and the k-Edge-Connected Network Design Problem (kESNDP). The experimental study of the parallelisation is presented after that. In the last part of this work, we present a two parallel exact algorithms: a distributed Branch-and-Bound and a distributed Branch-and-Cut. A series of experiments has been made on a cluster of 128 processors and interesting speedups has been reached in kHNDP resolution when k=3 and L=3.

Les travaux de thèse s’intéressent au réglage de la puissance active injectée dans le réseau, ce qui représente aujourd'hui l'une des problématiques principales du pilotage des parcs éoliens participant à la gestion du réseau. Dans le même temps, l'un des buts reste de maximiser la puissance extraite du vent en considérant les effets de couplage aérodynamique entre les éoliennes.La structure du contrôle-commande choisie est de type hiérarchisée et distribuée. Dans la première partie de la thèse, les travaux portent sur la commande de la turbine d'une éolienne autour des points de fonctionnement classiques mais également autour des points à puissance extraite réduite. En fait, cela relève d’une condition de fonctionnement nécessaire pour l'atteinte des objectifs imposés au pilotage d'un parc éolien.Dans la deuxième partie, le problème du contrôle à l'échelle d'un parc est posé sous la forme d'une optimisation distribuée parmi les turbines. Deux nouveaux algorithmes d'optimisation métaheuristique sont proposés et leur performance testée sur différents exemples de parcs éoliens. Les deux algorithmes s'appuient sur la méthode d'optimisation par essaim particulaire, qui est ici modifiée et adaptée pour les cas d'application aux systèmes multi agents. L'architecture de contrôlecommande globale est enfin évaluée en considérant les dynamiques des turbines contrôlées. Les simulations effectuées montrent des gains potentiels significatifs en puissance.Finalement, dans la troisième partie de la thèse, l'introduction d'une nouvelle étape de coopération au niveau des contrôleurs locaux des turbines, par l'utilisation de la technique de contrôle par consensus, permet d'améliorer les performances du système global. / In this PhD work we focus on the wind farm (WF) active power control since some of the new set grid requirements of interest can be expressed as specifications on its injection in the electric grid. Besides, one of our main objectives is related to the wind farm power maximization problem under the presence on non-negligible wake effect. The chosen WF control architecture has a two-layer hierarchical distributed structure. First of all, the wind turbine (WT) control is addressed. Here, a nonlinear controller lets a WT work in classic zones of functioning as well as track general deloaded power references. This last feature is a necessary condition to accomplish the WF control specifications. Secondly, the high level WF control problem is formulated as an optimization problem distributed among the WTs. Two novel distributed optimization algorithms are proposed, and their performance tested on different WF examples. Both are based on the well-known particle swarm optimization algorithm, which we modify and extend to be applicable in the multi-agent system framework. Finally, the overall WF control is evaluated by taking into account the WTs controlled dynamics. Simulations show potential significant power gains. Eventually, the introduction of a new control level in the hierarchical structure between the WF optimization and the WTs controllers is proposed. The idea is to let further cooperation among the WT local controllers, via a consensusbased technique, to enhance the overall system performance.

[pt] A solução do problema de planejamento da expansão da transmissão (PET) tem por objetivo geral identificar reforços a serem construídos na rede de forma a garantir a adequada interligação entre carga e geração, previstos para um determinado horizonte de estudo. No processo de solução desse problema, busca-se manter o equilíbrio ótimo entre os custos envolvidos (investimento e operação) e os níveis de qualidade e desempenho na operação do sistema reforçado. Nesse sentido, é proposta nesta dissertação de mestrado uma ferramenta de otimização especializada para solução do problema PET, a qual é baseada na técnica metaheurística Algoritmo Genético. A ferramenta proposta, denominada Algoritmo Genético Especializado (AGE-PET), faz uso de informações heurísticas fundamentadas em análises atualizadas de fluxo de potência da rede realizadas durante o processo evolutivo de solução do problema. Essas informações heurísticas são traduzidas por meio de índices de sensibilidade, os quais são integrados aos operadores genéticos inerentes à ferramenta, conduzindo a solução do problema na direção de planos de expansão de boa qualidade. Para análise e validação da metodologia proposta, é solucionado o problema PET estático de longo prazo, considerando o modelo linearizado DC com perdas ôhmicas e atendimento do critério de segurança N-1 para a rede de transmissão. Sistemas elétricos de transmissão com diferentes características e dimensões, incluindo um subsistema atual da rede interligada brasileira, são empregados nos estudos realizados. / [en] The main goal in the solution of the transmission expansion planning (TEP) is to identify reinforcements to be built in the network in order to guarantee the adequate interconnection between load and electric power generation, both foreseen for a given future planning horizon. In the process of solving this problem, the aim is to maintain the optimal balance between the costs involved (investment and operation) and the levels of quality and performance in the operation of the reinforced system. Thus, it is proposed in this dissertation a specialized optimization tool for solving the TEP problem, which is based on the metaheuristic Genetic Algorithm technique. The proposed tool, called Specialized Genetic Algorithm (SGA-TEP), makes use of heuristic information based on updated network power flow analyses carried out during the evolutionary process of solving the problem. This heuristic information is translated by means of sensitivity indices, which are integrated with the genetic operators inherent to the tool, leading to the solution of the problem in the direction of good quality expansion plans. For analysis and validation of the proposed methodology, the long-term static TEP problem is solved, considering the linearized DC model with ohmic losses and the compliance of the N-1 security criterion for the transmission network. Electric transmission systems with different characteristics and dimensions, including a recent subsystem of the Brazilian interconnected grid, are used in the case studies.

[pt] O Brasil é um dos principais produtores e exportadores de celulose e papel no mundo, beneficiando-se de condições climáticas e de solo favoráveis, além de investimentos substanciais em pesquisa. Um desafio significativo nesse setor é o Problema de Planejamento de Colheita Florestal (PPCF), semelhante a um derivado do Problema de Roteamento de Veículos (VRP), com uma frota heterogênea, demanda periódica e ganho de volume de madeira. Este estudo aborda o PPCF utilizando um modelo matemático de Programação Linear Inteira Mista (MILP) e a metaheurística Greedy Randomized Adaptive Search Procedure (GRASP) em cenários simulados e reais para otimizar o sequenciamento dos times de colheita entre as unidades produtivas. O objetivo é reduzir os custos operacionais e aumentar o crescimento do volume ao longo de um horizonte de planejamento de 12 meses, considerando também as restrições de janelas de tempo. Um total de 12 instâncias foram testadas para avaliar o desempenho do GRASP, sendo que a metaheurística superou o resultado do modelo MILP em nove casos. Além disso, três instâncias refletem cenários reais de uma grande empresa brasileira de celulose e papel. Quando comparado aos resultados da equipe de planejamento da empresa, o GRASP alcançou uma redução de até 61,9 por cento nos custos totais. Além disso, o GRASP fornece planos de colheita detalhados em um curto tempo de execução, reduzindo a carga de trabalho da equipe de planejamento e aumentando a flexibilidade na tomada de decisões. / [en] Brazil is one of the world s leading producers and exporters of pulp and paper, benefiting from favorable climatic and soil conditions, coupled with substantial investments in research. A significant challenge in this sector is the Forest Harvesting Planning Problem (FHPP), akin to a derivative of the Vehicle Routing Problem (VRP) featuring a heterogeneous fleet, periodic demand, and wood volume gain. This study addresses FHPP by employing Mixed Integer Linear Programming (MILP) modeling and the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic across real and simulated scenarios to optimize the sequencing of harvesting teams among stands. The objective is to reduce operational costs and enhance volume growth over a 12- month planning horizon, while also considering time windows and scheduling constraints. A total of 12 instances were tested to evaluate GRASP s performance, with the metaheuristic matching or outperforming the MILP model in nine cases. Additionally, three instances reflect real scenarios from a major Brazilian pulp and paper company. When compared against the company s planning team results, GRASP achieved up to a 61.9 percent reduction in total costs. Furthermore, GRASP provides detailed harvesting plans within a short execution time, reducing planning team workload and enhancing decision-making flexibility.