Desenvolvimento de técnicas eficientes de programação linear na utilização de metaheurísticas para o problema de planejamento da expansão de sistemas de transmissão

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Planejamento da expansão de sistemas de transmissão usando a metaheurística de busca em vizinhança variável

Pós-graduação em Engenharia Elétrica - FEIS

Métodos de solução para um problema de sequenciamento da produção com sincronismo de execução de tarefas

Pós-graduação em Engenharia Mecânica - FEG

Resolução do problema de sequenciamento de múltiplas linhas de montagem de modelos mistos em ambiente de produção enxuta

This work deals with the sequencing of Multi-Mixed-Model Assembly Lines in a lean manufacturing environment, where an operational structure where several kanbans support several mixed-model assembly lines, so that all assembly lines can receive parts or sub-assemblies from all suppliers. To optimize this system, the sequencing seeks to minimize the distance between the real consumption and the constant ideal consumption of parts or subassemblies, thereby reducing the scaling of kanbans and intermediate stocks. To solve the sequencing problems, the method Clustering Search was applied along with the metaheuristics Variable Neighborhood Search, Simulation Annealing and Iterative Local Search. Instances from the literature and generated instances were tested, thus allowing comparing the methods to each other and with other methods presented in the literature. The performance of the Clustering Search with Iterated Local Search stands out by the quality and robustness of their solutions, and mainly for its efficiency, whereas it converges to better results at a lower computational cost

Reconfiguração de sistemas de distribuição operando em vários níveis de demanda através de uma meta-heurística de busca em vizinhança variável

Pós-graduação em Engenharia Elétrica - FEIS

Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data

Mass spectrometry (MS) data provide a promising strategy for biomarker discovery. For this purpose, the detection of relevant peakbins in MS data is currently under intense research. Data from mass spectrometry are challenging to analyze because of their high dimensionality and the generally low number of samples available. To tackle this problem, the scientific community is becoming increasingly interested in applying feature subset selection techniques based on specialized machine learning algorithms. In this paper, we present a performance comparison of some metaheuristics: best first (BF), genetic algorithm (GA), scatter search (SS) and variable neighborhood search (VNS). Up to now, all the algorithms, except for GA, have been first applied to detect relevant peakbins in MS data. All these metaheuristic searches are embedded in two different filter and wrapper schemes coupled with Naive Bayes and SVM classifiers.

Formulações e algoritmos para o problema das p-medianas heterogêneo livre de penalidade

This work presents a new model for the Heterogeneous p-median Problem (HPM), proposed to recover the hidden category structures present in the data provided by a sorting task procedure, a popular approach to understand heterogeneous individual’s perception of products and brands. This new model is named as the Penalty-free Heterogeneous p-median Problem (PFHPM), a single-objective version of the original problem, the HPM. The main parameter in the HPM is also eliminated, the penalty factor. It is responsible for the weighting of the objective function terms. The adjusting of this parameter controls the way that the model recovers the hidden category structures present in data, and depends on a broad knowledge of the problem. Additionally, two complementary formulations for the PFHPM are shown, both mixed integer linear programming problems. From these additional formulations lower-bounds were obtained for the PFHPM. These values were used to validate a specialized Variable Neighborhood Search (VNS) algorithm, proposed to solve the PFHPM. This algorithm provided good quality solutions for the PFHPM, solving artificial generated instances from a Monte Carlo Simulation and real data instances, even with limited computational resources. Statistical analyses presented in this work suggest that the new algorithm and model, the PFHPM, can recover more accurately the original category structures related to heterogeneous individual’s perceptions than the original model and algorithm, the HPM. Finally, an illustrative application of the PFHPM is presented, as well as some insights about some new possibilities for it, extending the new model to fuzzy environments

A Decomposition, Construction and Post-Processing Approach for Nurse Rostering

This paper presents our work on decomposing a specific nurse rostering problem by cyclically assigning blocks of shifts, which are designed considering both hard and soft constraints, to groups of nurses. The rest of the shifts are then assigned to the nurses to construct a schedule based on the one cyclically generated by blocks. The schedules obtained by decomposition and construction can be further improved by a variable neighborhood search. Significant results are obtained and compared with a genetic algorithm and a variable neighborhood search approach on a problem that was presented to us by our collaborator, ORTEC bv, The Netherlands. We believe that the approach has the potential to be further extended to solve a wider range of nurse rostering problems.

Problema del agente viajero selectivo con restricciones adicionales

En esta tesis se introduce una variante del Problema del Agente Viajero Selectivo, también conocido en la literatura como Orienteering Problem (OP). En el OP se tiene un conjunto de clientes potenciales, a cada uno de los cuales se le asocia una puntuación o beneficio que recibe el agente al visitarlo, el objetivo es el de diseñar una ruta que comience y termine en el depósito y que maximice el puntaje colectado, tomando en cuenta que existe un límite máximo en la duración de la ruta. En este trabajo se consideran restricciones de conflictos entre clientes, es decir, si dos de ellos tienen conflicto, no pueden ser incluidos ambos en la ruta; por otra parte, existe un subconjunto de clientes que deben ser visitados de manera obligatoria. Se proponen dos modelos matemáticos del problema, cuya diferencia principal es la manera en que aborda la eliminación de ciclos. El primer modelo usa restricciones de tipo secuencial inspiradas en las propuestas por Miller et al. (1960) y el segundo utiliza restricciones basadas en flujo de múltiples productos y se basan en las restricciones propuestas por Wong (1980) y Claus (1984). Asimismo, se proponen dos algoritmos para la solución del problema planteado, el primero es de tipo heurístico y está basado en un esquema GRASP (Greedy Randomized Adaptive Search Procedure) reactivo, cuya fase de mejora es un método tipo VNS (Variable Neighborhood Search) general, el segundo es una estrategia de descomposición basada en generación de columnas. El desempeño de los algoritmos propuestos es evaluado a través de experimentos computacionales sobre un gran conjunto de instancias y los resultados obtenidos son comparados contra las soluciones ´optimas obtenidas al resolver los modelos matemáticos haciendo uso del solver Cplex 12.6.

Vehicle routing and location routing problems with minimum latency: algorithms and models

Latency can be defined as the sum of the arrival times at the customers. Minimum latency problems are specially relevant in applications related to humanitarian logistics. This thesis presents algorithms for solving a family of vehicle routing problems with minimum latency. First the latency location routing problem (LLRP) is considered. It consists of determining the subset of depots to be opened, and the routes that a set of homogeneous capacitated vehicles must perform in order to visit a set of customers such that the sum of the demands of the customers assigned to each vehicle does not exceed the capacity of the vehicle. For solving this problem three metaheuristic algorithms combining simulated annealing and variable neighborhood descent, and an iterated local search (ILS) algorithm, are proposed. Furthermore, the multi-depot cumulative capacitated vehicle routing problem (MDCCVRP) and the multi-depot k-traveling repairman problem (MDk-TRP) are solved with the proposed ILS algorithm. The MDCCVRP is a special case of the LLRP in which all the depots can be opened, and the MDk-TRP is a special case of the MDCCVRP in which the capacity constraints are relaxed. Finally, a LLRP with stochastic travel times is studied. A two-stage stochastic programming model and a variable neighborhood search algorithm are proposed for solving the problem. Furthermore a sampling method is developed for tackling instances with an infinite number of scenarios. Extensive computational experiments show that the proposed methods are effective for solving the problems under study.

Planejamento da expansão de sistemas de distribuição usando a metaheurística de busca em vizinhança variável

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Sequenciamento de lotes em prensas de alta capacidade com tempo de setup dependente da sequência

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Heurística especializada aplicada na alocação ótima de bancos de capacitores em sistemas de distribuição radial

Pós-graduação em Engenharia Elétrica - FEIS

Méthode de recherche à grand voisinage pour un problème de tournées de véhicules avec flotte privée et transporteur externe

Dans ce mémoire, nous étudions un problème de tournées de véhicules dans lequel une flotte privée de véhicules n’a pas la capacité suffisante pour desservir les demandes des clients. Dans un tel cas, on fait appel à un transporteur externe. Ce dernier n’a aucune contrainte de capacité, mais un coût est encouru lorsqu’un client lui est affecté. Il n’est pas nécessaire de mettre tous les véhicules de la flotte privée en service si cette approche se révèle plus économique. L’objectif consiste à minimiser le coût fixe des véhicules, puis le coût variable de transport et le coût chargé par le transporteur externe. Notre travail consiste à appliquer la métaheuristique de recherche adaptative à grand voisinage sur ce problème. Nous comparons nos résultats avec ceux obtenus précédemment avec différentes techniques connues sur les instances de Christofides et celles de Golden.

Méthode de recherche à grand voisinage pour un problème de tournées de véhicules avec flotte privée et transporteur externe

Dans ce mémoire, nous étudions un problème de tournées de véhicules dans lequel une flotte privée de véhicules n’a pas la capacité suffisante pour desservir les demandes des clients. Dans un tel cas, on fait appel à un transporteur externe. Ce dernier n’a aucune contrainte de capacité, mais un coût est encouru lorsqu’un client lui est affecté. Il n’est pas nécessaire de mettre tous les véhicules de la flotte privée en service si cette approche se révèle plus économique. L’objectif consiste à minimiser le coût fixe des véhicules, puis le coût variable de transport et le coût chargé par le transporteur externe. Notre travail consiste à appliquer la métaheuristique de recherche adaptative à grand voisinage sur ce problème. Nous comparons nos résultats avec ceux obtenus précédemment avec différentes techniques connues sur les instances de Christofides et celles de Golden.