Le succès dans l'utilisation de méthodes exactes d’optimisation combinatoire pour des problèmes de grande taille est encore limité à certains problèmes ou à des classes spécifiques d'instances de problèmes. Une approche alternative consiste soit à utiliser des métaheuristiques ou des matheuristiques qui reposent en partie sur des méthodes exactes. Dans le contexte de l'optimisation combinatoire, nous nous intéressons des heuristiques permettant de choisir les heuristiques appliquées au problème traité. Dans cette thèse, nous nous concentrons sur l'optimisation à l’aide d’hyperheuristiques pour des problèmes logistiques. Nous proposons un cadre hyperheuristique qui effectue une recherche dans l'espace des algorithmes heuristiques et apprend comment changer l'heuristique courante systématiquement tout au long du processus de telle sorte qu'une bonne séquence d'heuristiques permet d’obtenir des solutions de haute qualité. Nous étudions plus particulièrement deux problèmes en logistique pour lesquels nous proposons des HHs: un problème de planification d’interventions sur des puits de forage et un problème conjoint de localisation de hubs et de routage. Ensuite, nous comparons les performances de plusieurs HH décrites dans la littérature pour le second problème abordé reposant sur différentes méthodes de sélection heuristique telles que la sélection aléatoire, la fonction de choix, une approche de Q-Learning et un algorithme de colonie de fourmis. Les résultats numériques prouvent l'efficacité de HHs pour les deux problèmes traités, et la pertinence d'inclure l'information venant d’une relaxation de Lagrangienne pour le deuxième problème. / Success in using exact methods for large scale combinatorial optimization is still limited to certain problems or to specific classes of instances of problems. The alternative way is either using metaheuristics or matheuristics that rely on exact methods in some ways. In the context of combinatorial optimization, we are interested in heuristics to choose heuristics invoked to solve the addressed problem. In this thesis, we focus on hyperheuristic optimization in logistic problems. We focus on proposing a hyperheuristic framework that carries out a search in the space of heuristic algorithms and learns how to change the incumbent heuristic in a systematic way along the process in such a way that a good sequence of heuristics produces high quality solutions. We propose HHs for two problems in logistics: the workover rig scheduling problem and the hub location routing problem. Then, we compare the performances of several HHs described in the literature for the latter problem, which embed different heuristic selection methods such as a random selection, a choice function, a Q-Learning approach, and an ant colony based algorithm. The computational results prove the efficiency of HHs for the two problems in hand, and the relevance of including Lagrangian relaxation information for the second problem.

Este trabalho propõe uma metodologia baseada em técnicas inteligentes capaz de fornecer uma coordenação otimizada de relés direcionais de sobrecorrente instalados em sistemas de energia elétrica. O problema é modelado como um caso de programação não linear inteira mista, em que os relés permitem ajustes discretizados de múltiplos de tempo e/ou múltiplos de corrente. A solução do problema de otimização correspondente é obtida através de uma metaheurística nomeada como Discrete Particle Swarm Optimization. Na literatura técnico-científica esse problema geralmente é linearizado e aplicam-se arredondamentos das variáveis discretas. Na metodologia proposta, as variáveis discretas são tratadas adequadamente para utilização na metaheurística e são apresentados os resultados que foram comparados com os obtidos pelo modelo clássico de otimização implementado no General Algebraic Modeling System (GAMS). Tendo em vista os aspectos observados, o método permite ao engenheiro de proteção ter um subsídio adicional na tarefa da coordenação dos relés direcionais de sobrecorrente, disponibilizando uma técnica eficaz e de fácil aplicabilidade ao sistema elétrico a ser protegido, independentemente da topologia e condição operacional. / This work proposes a methodology that based on intelligent technique to obtain an optimized coordination of directional overcurrent relays in electric power systems. The problem is modeled as a mixed integer nonlinear problem, because the relays allows a discrete setting of time and/or current multipliers. The solution of the proposed optimization problem is obtained from the proposed metaheuristic named as Discrete Particle Swarm Optimization. In scientific and technical literature this problem is usually linearized and discrete variables are rounded off. In the proposed method, the discrete variables are modeled adequately in the metaheuristic and the results are compared to the classical optimization solvers implemented in General Algebraic Modeling System (GAMS). The method provides an important method for helping the engineers in to coordinate directional overcurrent relays in a very optimized way. It has high potential for the application to realistic systems, regardless of topology and operating condition.

This work has been supported by the European FACETS-ITN project. Within the frameworkof this project, we contribute to the simulation of cortical cell types (employingexperimental electrophysiological data of these cells as references), using a specific VLSIneural circuit to simulate, at the single cell level, the models studied as references in theFACETS project. The real-time intrinsic properties of the neuromorphic circuits, whichprecisely compute neuron conductance-based models, will allow a systematic and detailedexploration of the models, while the physical and analog aspect of the simulations, as opposedthe software simulation aspect, will provide inputs for the development of the neuralhardware at the network level. The second goal of this thesis is to contribute to the designof a mixed hardware-software platform (PAX), specifically designed to simulate spikingneural networks. The tasks performed during this thesis project included: 1) the methodsused to obtain the appropriate parameter sets of the cortical neuron models that can beimplemented in our analog neuromimetic chip (the parameter extraction steps was validatedusing a bifurcation analysis that shows that the simplified HH model implementedin our silicon neuron shares the dynamics of the HH model); 2) the fully customizablefitting method, in voltage-clamp mode, to tune our neuromimetic integrated circuits usinga metaheuristic algorithm; 3) the contribution to the development of the PAX systemin terms of software tools and a VHDL driver interface for neuron configuration in theplatform. Finally, it also addresses the issue of synaptic tuning for future SNN simulation.

Made available in DSpace on 2014-12-17T15:48:00Z (GMT). No. of bitstreams: 1 GustavoAS_DISSERT.pdf: 3060399 bytes, checksum: 659289bc757f2443a1b6902094747b13 (MD5) Previous issue date: 2012-02-27 / This work approaches the Scheduling Workover Rigs Problem (SWRP) to maintain the wells of an oil field, although difficult to resolve, is extremely important economical, technical and environmental. A mathematical formulation of this problem is presented, where an algorithmic approach was developed. The problem can be considered to find the best scheduling service to the wells by the workover rigs, taking into account the minimization of the composition related to the costs of the workover rigs and the total loss of oil suffered by the wells. This problem is similar to the Vehicle Routing Problem (VRP), which is classified as belonging to the NP-hard class. The goal of this research is to develop an algorithmic approach to solve the SWRP, using the fundamentals of metaheuristics like Memetic Algorithm and GRASP. Instances are generated for the tests to analyze the computational performance of the approaches mentioned above, using data that are close to reality. Thereafter, is performed a comparison of performance and quality of the results obtained by each one of techniques used / O trabalho em quest?o aborda o Problema da Programa??o das Sondas de Produ??o (PPSP) para atender os po?os de um campo de petr?leo. Embora de dif?cil resolu??o, ele ? de extrema import?ncia econ?mica, t?cnica e ambiental. Uma formula??o matem?tica deste problema ? apresentada, assim como desenvolvida uma abordagem algor?tmica. O problema abordado pode ser considerado como o de encontrar o melhor escalonamento de atendimento aos po?os pelas sondas, levando em considera??o a minimiza??o da composi??o dos custos relativos ?s sondas e da perda total da produ??o de petr?leo associada aos po?os que est?o aguardando por atendimento. Tal problema assemelha-se ao Problema de Roteamento de Ve?culos (PRV), que ? classificado como pertencente ? classe de problemas NP-Dif?cil. O objetivo da presente pesquisa ? desenvolver uma abordagem algor?tmica para resolver o PPSP, utilizando os fundamentos de metaheur?sticas como o Algoritmo Mem?tico e o GRASP. Inst?ncias s?o geradas para a realiza??o dos testes computacionais para an?lise do desempenho das abordagens acima citadas, utilizando dados que se aproximam da realidade. A partir da?, ? realizada uma compara??o de desempenho e qualidade dos resultados obtidos por cada uma das t?cnicas utilizadas

Made available in DSpace on 2014-12-17T15:48:03Z (GMT). No. of bitstreams: 1 AntonioAFN_DISSERT.pdf: 3187796 bytes, checksum: c8d44014d0b75e991f4f3b3473a8dcd5 (MD5) Previous issue date: 2012-08-24 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Committees of classifiers may be used to improve the accuracy of classification systems, in other words, different classifiers used to solve the same problem can be combined for creating a system of greater accuracy, called committees of classifiers. To that this to succeed is necessary that the classifiers make mistakes on different objects of the problem so that the errors of a classifier are ignored by the others correct classifiers when applying the method of combination of the committee. The characteristic of classifiers of err on different objects is called diversity. However, most measures of diversity could not describe this importance. Recently, were proposed two measures of the diversity (good and bad diversity) with the aim of helping to generate more accurate committees. This paper performs an experimental analysis of these measures applied directly on the building of the committees of classifiers. The method of construction adopted is modeled as a search problem by the set of characteristics of the databases of the problem and the best set of committee members in order to find the committee of classifiers to produce the most accurate classification. This problem is solved by metaheuristic optimization techniques, in their mono and multi-objective versions. Analyzes are performed to verify if use or add the measures of good diversity and bad diversity in the optimization objectives creates more accurate committees. Thus, the contribution of this study is to determine whether the measures of good diversity and bad diversity can be used in mono-objective and multi-objective optimization techniques as optimization objectives for building committees of classifiers more accurate than those built by the same process, but using only the accuracy classification as objective of optimization / Comit?s de classificadores podem ser empregados para melhorar a acur?cia de sistemas de classifica??o, ou seja, diferentes classificadores aplicados ? solu??o de um mesmo problema podem ser combinados gerando um sistema de maior acur?cia, denominado de comit?s de classificadores. Para que se obtenha sucesso ? necess?rio que os classificadores apresentem erros em diferentes objetos do problema para que assim os erros de um classificador sejam suprimidos pelo acerto dos demais na aplica??o do m?todo de combina??o do comit?. A caracter?stica dos classificadores de errarem em objetos diferentes ? denominada de diversidade. No entanto, as maiorias das medidas de diversidade n?o conseguiam descrever essa import?ncia. Recentemente, foram propostas duas medidas de diversidade (boa e m? diversidade) as medidas de boa e m? diversidade com o objetivo de auxiliar a gera??o de comit?s mais acurados. Este trabalho efetua uma an?lise experimental dessas medidas aplicadas diretamente na constru??o de comit?s de classificadores. O m?todo de constru??o adotado ? modelado como um problema de busca pelo melhor conjunto de caracter?sticas das bases de dados do problema e pelo melhor conjunto de membros do comit? a fim de encontrar o comit? de classificadores que apresente ? maior acur?cia de classifica??o. Esse problema ? resolvido atrav?s de t?cnicas de otimiza??o metaheur?sticas, nas vers?es mono e multiobjetivo. S?o efetuadas an?lises estat?sticas para verificar se usar ou adicionar as medidas de boa e m? diversidade como objetivos de otimiza??o resulte comit?s mais acurados. Assim, a contribui??o desse trabalho ? determinar se as medidas de boa e m? diversidade podem ser utilizadas em t?cnicas de otimiza??o mono e multiobjetivo como objetivos de otimiza??o para constru??o de comit?s de classificadores mais acurados que aqueles constru?dos pelo mesmo processo, por?m utilizando somente a acur?cia de classifica??o como objetivo de otimiza??o

Le management de la chaîne logistique concerne un large éventail d’activités. Nombreuses ceux qui ont un caractère incertain apportant souvent des conséquences inattendues. Malgré cela, l’incertitude est fréquemment non considérée dans la gestion de la chaîne logistique traditionnelle. En plus de l’incertitude, l’indisponibilité des ressources augmentera la complexité du problème. En prenons en compte les contraintes d’incertitude et de disponibilité nous étudions le management de la chaîne logistique selon différents aspects. Cette thèse représente une tentative de recherche afin d’aborder ce problème d’une façon systématique et complète et nous espérons que notre travail contribuera aux futurs travaux de recherche et sera utile aux gestionnaires de la chaîne logistique. Nous nous concentrons sur trois sources classiques de l’incertitude ; celle de la demande, celle la fabrication et celle liée à la distribution. Pour chaque source d’incertitude, nous analysons ses causes et ses impacts sur les performances de la chaîne logistique. L’incertitude est spécifiée dans des problèmes classiques concrets et des approches sont proposées pour les résoudre. Nous nous sommes également focalisés sur le problème bi-niveau de vendeur de journaux qui représente une chaîne logistique miniature, concerné par une double incertitude. Les méthodes utilisées offrent une bonne démonstration du traitement des variables incertaines dans les problèmes de décision / Supply chain management involves a wide range of activities. Among most of them, uncertainty exists inherently and always brings some consequence not expected. However, uncertainty is not considered much in conventional supply chain management. In the case where availability of resources is not what we expect, complexity of supply chain management increases. Taking constraints of uncertainty and availability into account, we aim to discuss supply chain management from different aspects. This thesis is an attempt of systematic and complete research from this point and we would like to offer some references to researchers and managers in supply chain. We focus on three classic sources of uncertainty: demand, manufacturing and distribution. For each source of uncertainty, we analyze its cause and its impact to the performance of the supply chain. Uncertainty is specified into concrete classic problem and an approach is proposed to solve it. Furthermore, bi-level newsboy problem as a miniature of supply chain, is focused under double uncertain environment. Treating uncertain variables is actually a treatment on operational level. The methods used offer good demonstration in treating uncertain variables in decision problems

Pour résoudre des problèmes d'optimisation discret de type boîte noire, de nombreux algorithmes stochastiques tels que les algorithmes évolutionnaires ou les métaheuristiques existent et se révèlent particulièrement efficaces selon le problème à résoudre. En fonction des propriétés observées du problème, choisir l'algorithme le plus pertinent est un problème difficile. Dans le cadre original des environnements de calcul parallèle et distribué, nous proposons et analysons différentes stratégies adaptative de sélection d'algorithme d'optimisation. Ces stratégies de sélection reposent sur des méthodes d'apprentissage automatique par renforcement, issu du domaine de l'intelligence artificielle, et sur un partage d'information entre les noeuds de calcul. Nous comparons et analysons les stratégies de sélection dans différentes situations. Deux types d'environnement de calcul distribué synchrone sont abordés : le modèle en île et le modèle maître-esclave. Sur l'ensemble des noeuds de manière synchrone à chaque itération la stratégie de sélection adaptative choisit un algorithme selon l'état de la recherche de la solution. Dans une première partie, deux problèmes OneMax et NK, l'un unimodal et l'autre multimodal, sont utilisés comme banc d'essai de ces travaux. Ensuite, pour mieux saisir et améliorer la conception des stratégies de sélection adaptatives, nous proposons une modélisation du problème d'optimisation et de son opérateur de recherche locale. Dans cette modélisation, une caractéristique importante est le gain moyen d'un opérateur en fonction de la fitness de la solution candidate. Le modèle est utilisé dans le cadre synchrone du modèle maître-esclave. Une stratégie de sélection se décompose en trois composantes principales : l'agrégation des récompenses échangées, la technique d'apprentissage et la répartition des algorithmes sur les noeuds de calcul. Dans une dernière partie, nous étudions trois scénarios et nous donnons des clés de compréhension sur l'utilisation pertinente des stratégies de sélection adaptative par rapport aux stratégies naïves. Dans le cadre du modèle maître-esclave, nous étudions les différentes façons d'agréger les récompenses sur le noeud maître, la répartition des algorithmes d'optimisation sur les noeuds de calcul et le temps de communication. Cette thèse se termine par des perspectives pour le domaine de l'optimisation stochastique adaptative distribuée. / To solve discrete optimization problems of black box type, many stochastic algorithms such as evolutionary algorithms or metaheuristics exist and prove to be particularly effective according to the problem to be solved. Depending on the observed properties of the problem, choosing the most relevant algorithm is a difficult problem. In the original framework of parallel and distributed computing environments, we propose and analyze different adaptive optimization algorithm selection strategies. These selection strategies are based on reinforcement learning methods automatic, from the field of artificial intelligence, and on information sharing between computing nodes. We compare and analyze selection strategies in different situations. Two types of synchronous distributed computing environment are discussed : the island model and the master-slave model. On the set of nodes synchronously at each iteration, the adaptive selection strategy chooses an algorithm according to the state of the search for the solution. In the first part, two problems OneMax and NK, one unimodal and the other multimodal, are used as benchmarks for this work. Then, to better understand and improve the design of adaptive selection strategies, we propose a modeling of the optimization problem and its local search operator. In this modeling, an important characteristic is the average gain of an operator according to the fitness of the candidate solution. The model is used in the synchronous framework of the master-slave model. A selection strategy is broken down into three main components : the aggregation of the rewards exchanged, the learning scheme and the distribution of the algorithms on the computing nodes. In the final part, we study three scenarios, and we give keys to understanding the relevant use of adaptive selection strategies over naïve strategies. In the framework of the master-slave model, we study the different ways of aggregating the rewards on the master node, the distribution of the optimization algorithms of the nodes of computation and the time of communication. This thesis ends with perspectives in the field of distributed adaptive stochastic optimization.

[pt] O planejamento da expansão da transmissão (PET) visa identificar os novos reforços a serem implementados na rede do sistema elétrico de potência, necessá-rios para assegurar uma adequada interligação entre a demanda e a geração do sistema, ambas previstas para o horizonte de planejamento. Um bom plano de expansão deve garantir o equilíbrio entre os custos de investimento e operação, mantendo um nível satisfatório de continuidade no fornecimento de energia. En-tretanto, a identificação de boas soluções para o PET tem se tornado uma tarefa cada vez mais difícil. Isso se deve, principalmente, às características e dimensões dos sistemas atuais, incluindo o aumento na dependência de fontes renováveis, e à não linearidade e natureza combinatória do problema de otimização. Nesta dissertação é proposta uma nova metodologia para resolver o proble-ma PET com alta penetração de energia renovável, em particular a eólica. A me-todologia é baseada na aplicação de uma nova ferramenta de otimização para so-lução do PET estático, a qual é classificada como metaheurística construtiva, onde soluções viáveis de boa qualidade são paralelamente construídas a partir da topo-logia inicial, por meio de adições graduais de reforços mais atrativos para a rede. Outras heurísticas são também utilizadas. Ênfase é dada à modelagem de cenários de geração eólica, que representam a energia renovável da rede a ser planejada, a qual deverá permitir uma operação flexível e adaptada à intermitência destas fon-tes. São utilizados o critério de segurança N-1 e o modelo linear DC de rede, com a consideração de perdas ôhmicas. Uma variante do sistema IEEE RTS, com inserção de fontes eólicas, é utilizada para testar a metodologia proposta. / [en] Transmission expansion planning (TEP) aims to identify the new reinforce-ments to be installed in the electric power system, necessary to ensure an adequate interconnection between demand and generation of the system, both foreseen for the planning horizon. A good expansion plan should ensure a balance between investment and operating costs, while maintaining a satisfactory level of continui-ty in the energy supply. However, identifying good expansion solutions for TEP has become an increasingly difficult task. This is mainly due to the characteristics and dimensions of the current systems, including the increase in the dependence of renewable sources, and the nonlinearity and combinatorial nature of the optimi-zation problem. In this dissertation, a new methodology is proposed to solve the TEP prob-lem with high penetration of renewable energy, in particular wind power. The methodology is based on the application of a new optimization tool for static TEP solution, which is classified as a constructive metaheuristic, where feasible solu-tions of good quality are simultaneously constructed from the initial topology of the network, through incremental additions of reinforcements more attractive to the grid. Other heuristics are also used. Emphasis is given to the modeling of wind power scenarios, which represent the renewable energy of the network to be planned, which should allow a flexible operation and adapted to the intermittency of these sources. The security criterion N-1 and the linear DC network model are used, with the consideration of ohmic losses. A variant of the IEEE RTS sys-tem, with insertion of wind sources, is used to test the proposed methodology.

Le Système Électrique est soumis ces dernières années à plusieurs évolutions, depuis la dérégulationdu marché d'énergie à l'intégration de plus en plus importante de Générateurs Dispersés (GED). Ainsi,dans le cadre du concept de Smart Grid, les nouvelles technologies de l'information et de lacommunication (NTIC) offrent de nouvelles perspectives pour la gestion et l'exploitation des réseauxde distribution.Dans ce contexte, de nouveaux outils sont étudiés. Encore appelés Fonctions Avancéesd’Automatisation (FAA), le but principal de ces outils est d’utiliser tous les composants du réseau dedistribution de manière coordonnée en vue de les rendre plus actifs, flexibles et d’augmenter leurefficacité opérationnelle. Dans notre cas, nous avons étudié les fonctions associées à la reconfigurationen régime normal, du réglage de la tension et l’hybridation de ces deux derniers, tout en tenant comptede la présence des GED. En partant du comportement physique inhérent aux composants du réseau,plusieurs modèles ont été proposés. Certains sont tirés de la théorie des graphes et d’autres sur l’outilpuissant de la reformulation mathématique pour « convexifier » nos modèles. Cette modélisationadoptée répond à la fois à la nécessité de prendre en compte tous les moyens de réglages qui peuventêtre discrets (prises des transformateurs avec régleurs en charge ou des gradins de condensateurs),binaires (état de connectivité des composants) et continues (puissance réactive de la DG) et par lechoix des outils et des algorithmes d'optimisation mixte. En effet, la complexité de ces problèmes sonttelles que nous avons exploré à la fois des algorithmes méta-heuristiques (ACF : Algorithme desColonies de Fourmis) que déterministes (Décomposition de Benders Généralisée, Algorithme duBranch and Cut). / The Electric Power System is undergoing a lot of evolutions in recent years, including the energymarket deregulation and the increasing integration of Dispersed Generators (DG). Therefore, withinthe framework of Smart Grid concept, the New Information and Communication Technologies (NICT)provide new perspectives to manage and operate distribution networks.In this context, new tools, called Advanced Distribution Automation functions (ADA, are beingstudied). The main objective of these tools is to use all the distribution network components in acoordinated manner to make them more active and flexible, in addition to increasing their operationalefficiency. In our case, we studied the functions associated with the reconfiguration problem, thevoltage control problem and the hybridization of these two, while taking into account the presence ofthe DG. Based on the inherent components of network physical models, several models have beenproposed. Some are derived from the graph theory and others use powerful mathematicalreformulation to make our models convex. The adopted models answer to the necessity of taking intoaccount all regulation means, which can be discrete (On Load Tap-Changer and capacitor banks),binary (components connectivity such as lines or transformers) and continuous (DG reactive power ),and by the choice of tools and algorithms of mixed optimization. Indeed, the complexity of theseproblems is such that we have explored both algorithms: meta-heuristic (ACA, Ant Colony Algorithm)and deterministic (Generalized Benders Decomposition, Branch and Cut Algorithm).

[pt] A dissertação aborda um problema real de reprogramação de uma frota de embarcações do tipo PLSV (Pipe Laying Support Vessel), responsáveis pelas interligações de poços petrolíferos submarinos. O cronograma de curto prazo dessas embarcações está sujeito à inúmeras incertezas inerentes às operações realizadas, acarretando em ociosidade nas embarcações ou postergações na produção de petróleo, que podem resultar em prejuízo de milhões de reais. Uma metaheurística ILS (Iterated Local Search) é proposta para atender a frequente demanda por reprogramações dos PLSVs. O método é composto de uma fase inicial de viabilização, para tratar potenciais inconsistências nas programações. Na sequência, iterativamente, são realizadas perturbações na solução por meio de movimentos de swap e aplicada uma busca local baseada na vizinhança insert, a fim de fugir de ótimos locais e encontrar soluções que aprimorem o cronograma. Foram feitos experimentos com diferentes parâmetros e critérios do ILS, sendo definidas duas abordagens aplicadas a dez instâncias oriundas de uma programação real de PLSVs. A partir de uma função de avaliação, capaz de medir o impacto operacional na programação, o ILS proporcionou uma melhoria média nos cronogramas acima de 91 por cento, quando comparados aos cronogramas originais. As soluções foram obtidas em um tempo computacional médio de 30 minutos, aderente ao processo da companhia. Em função dos resultados alcançados, o método provou ser uma boa base para uma ferramenta de apoio à decisão para a reprogramação dos PLSVs. / [en] This dissertation addresses a real-life rescheduling problem of a Pipe Laying Support Vessels (PLSVs) fleet, in charge of subsea oil wells interconnections. The short-term schedule of these vessels is subject to uncertainties inherent to its operations, resulting in ships idleness or delays in oil production, which may lead to losses of millions of Brazilian Reais. A method based on the ILS (Iterated Local Search) metaheuristic is proposed to meet the frequent demand of PLSVs rescheduling. The first step of this method aims to find a feasible initial solution from an incoming schedule with potencial inconsistencies. The following steps consists in, iteratively, performing a perturbation on a solution through swap movements and applying a local search based on the insertion neighborhood, in order to escape from local optimal and find better solutions. Extensive preliminary experiments were conducted considering different ILS parameters setups. The two most performing setups were selected and applied to ten instances of a real PLSV schedule. Taking into account an objective function that measures the operational impact on schedules, the ILS provided an average improvement above 91 percent in schedules when compared to the original planning. These solutions were obtained in an average computational time of 30 minutes, which fits in the company process. The obtained results showed that the proposed method might be a basis for a decision support tool for the PLSVs rescheduling problem.