The loading problem in a Flexible Manufacturing System (FMS) lies in the allocation of operations and associated cutting tools to machines for a given set of parts subject to capacity constraints. This dissertation proposes a hierarchical approach to the machine loading problem when the workload and tool magazine capacity of each machine are restrained. This hierarchical approach reduces the maximum workload of the machines by partially grouping them. This research deals with situations where different groups of machines performing the same operation require different processing times and this problem is formulated as an integer linear problem. This work proposes a solution that is comprised of two phases. In the first phase (Phase I), demand is divided into batches and then operations are allocated to groups of machines by using a heuristic constrained by the workload and tool magazine capacity of each group. The processing time of the operation is different for each machine group, which is composed of the same identical machines; however, these machines can perform different sets of operations if tooled differently. Each machine and each group of machines has a limited time for completing an operation. Operations are allocated to groups based on their respective workload limits. In the second phase (Phase II), demand is divided into batches again and operations are assigned to machines based on their workload and tool magazine capacity defined by Longest Processing Time (LPT) and Multifit algorithms. In Phase II, like Phase I, partial grouping is more effective in balancing the workload than total grouping. In partial grouping, each machine is tooled differently, but they can assist one another in processing each individual operation. Phase I demonstrates the efficiency of allocating operations to each group. Phase II demonstrates the efficiency of allocating operations to each machine within each group. This two-phase solution enhances routing flexibility with the same or a smaller number of machines through partial grouping rather than through total grouping. This partial grouping provides a balanced solution for problems involving a large number of machines. Performance of the suggested loading heuristics is tested by means of randomly generated tests.

The loading problem in a Flexible Manufacturing System (FMS) lies in the allocation of operations and associated cutting tools to machines for a given set of parts subject to capacity constraints. This dissertation proposes a hierarchical approach to the machine loading problem when the workload and tool magazine capacity of each machine are restrained. This hierarchical approach reduces the maximum workload of the machines by partially grouping them. This research deals with situations where different groups of machines performing the same operation require different processing times and this problem is formulated as an integer linear problem. This work proposes a solution that is comprised of two phases. In the first phase (Phase I), demand is divided into batches and then operations are allocated to groups of machines by using a heuristic constrained by the workload and tool magazine capacity of each group. The processing time of the operation is different for each machine group, which is composed of the same identical machines; however, these machines can perform different sets of operations if tooled differently. Each machine and each group of machines has a limited time for completing an operation. Operations are allocated to groups based on their respective workload limits. In the second phase (Phase II), demand is divided into batches again and operations are assigned to machines based on their workload and tool magazine capacity defined by Longest Processing Time (LPT) and Multifit algorithms. In Phase II, like Phase I, partial grouping is more effective in balancing the workload than total grouping. In partial grouping, each machine is tooled differently, but they can assist one another in processing each individual operation. Phase I demonstrates the efficiency of allocating operations to each group. Phase II demonstrates the efficiency of allocating operations to each machine within each group. This two-phase solution enhances routing flexibility with the same or a smaller number of machines through partial grouping rather than through total grouping. This partial grouping provides a balanced solution for problems involving a large number of machines. Performance of the suggested loading heuristics is tested by means of randomly generated tests.

Background Loading efficiency within logistics is a research area which has been given a lot of attention by researchers during recent years. Pallet Loading Problem has been presented as a mean to tackle the loading efficiency issue in this area. Loading efficiency can be measured in terms of fill rate of the load and the number of reclaimed gods. Different loading methods presents advantages and disadvantages regarding the two presented areas fill rate and returned gods.      Purpose The purpose with this study is to examine the different issues related to implementing an efficient pallet loading method, with the help of a comparative study of alternative loading methods. In order to accomplish this, different key factors are located and identified in which the relation between these factors is displayed. Research in this area has highlighted that a streamlining of the loading process has assisted organizations in reducing their costs. The cost reduction is an effect of the reduced transport and reclaimed gods.   Methods In this comparative study of alternative loading methods, key factors in different loading methods were identified and analyzed. The data for the three key factors were collected through archival research, unstructured interviews and observations of the different procedures. The analytic tests of the data where conducted through SPSS with the likes of descriptive statistic, non-parametric t-test, correlation and logistic regression.     Results In the result of this thesis, the differences of studied loading methods are presented based on the defined key factors. Each loading method is individually analysed and later compared to the other alternative loading method in order to decide which method is the most efficient, based on the presented key factors. The analyses in the tests presents a connection between fill rate and claims but also between claims and loading method.     Conclusions Between the two presented loading methods, free pallet loading and standing pallet loading, there’s a significant difference based on the analysed key factors. Free pallet loading is concluded to be the more efficient loading method in each of the analysed categories fill rate, claims and cost of claims. This concludes free pallet loading to be the more sustainable and economically beneficial loading method. / Bakgrund Lastningseffektivitet inom logistiken är ett område som dem senaste år fått mycket uppmärksamhet där pallastning av produkter är ett centralt område. En lastningseffektivitet kan mätas i den totala fyllnadsgraden för lastningen samt vilken skaderisk det medför. Två centrala begrepp vilka studerats för lastning är Pallet Loading Problem samt Reclaimed Gods.            Ovanstående ämnesområden presenterar fördelar samt nackdelar med olika lastningsmetoder.   Syfte Syftet med denna studie är att undersöka problematiken kring att tillämpa en effektiv pallastning med hjälp av en jämförelsestudie kring alternativa lastningsmetoder. Detta då för att identifiera och kartlägga nyckelfaktorer samt sambandet mellan dessa i en lastningsprocess. Forskning visar på att en effektivisering av lastningsprocessen har hjälpt olika företag att reducera sina kostnader, genom en minskning av transport samt minimering av skaderisk för det lastade godset.   Metod Tillvägagångssättet för denna studie var att göra en jämförelsestudie där nyckelfaktorer i olika lastningsmetoder skall analyseras. Tre olika faktorer för lastning analyserades, där underlag för analysen införskaffades ur organisationens datasamling genom arkivforskning, observationer samt ostrukturerade intervjuer. Analyserna gjordes i SPSS genom de olika dataanalystesterna, deskriptiv statistik, icke parametriskt t-test, korrelation samt logistisk regression.   Resultat Resultatet av studien visar hur lastning skiljer sig åt med avseende på de tre olika nyckelkoncepten beroende på vilken metod som ligger till grund. Respektive lastningsmetod analyseras för sig själv för att sedan jämföras, detta då för att identifiera vilken som är effektivast beroende på nyckelfaktorer. Resultatet presenterar ett samband mellan claims och fyllnadsgraden samt mellan claims och metodval.    Slutsats Slutsatsen av denna studie är att bland de två presenterade lastningsmetoderna, löst liggande samt stående pallastning finns en signifikant skillnad sett till nyckelfaktorerna vilka undersökts. Löst liggande pallastning är en effektivare modell sett till samtliga av de undersökta nyckelfaktorerna det vill säga fyllnadsgrad, claims samt kostnaden vilken uppkommer i samband med claims. Detta medför att löst liggande lastning är den mer gynnsamma lastmetoden sett till både ett hållbarhets och ekonomiskt perspektiv.

Este trabalho aborda um problema ainda não explorado na literatura denominado 3L-FSMVRPTW (three-dimensional loading fleet sizing and mix vehicle routing problem with time windows), que compreende resolver simultaneamente o roteamento e carregamento tridimensional de veículos considerando frota heterogênea e janela de tempo. Foi desenvolvido um algoritmo específico para resolver o problema, denominado 3DC. Neste algoritmo foram introduzidas algumas inovações, entre elas, um novo operador de busca local (k-IntensiveSwap) e uma nova heurística de carregamento de contêiner. O algoritmo foi comparado aos melhores resultados disponíveis na literatura para problemas particulares ao apresentado. Houve bom desempenho no caso do CLP (container loading problem), bom resultado na redução do tamanho de frota no caso do 3L-VRP (threedimensional loading vehicle routing problem) e desempenho superior ao problema mais complexo estudado, o 3L-VRPTW (three-dimensional loading vehicle routing problem with time windows). Finalmente, apresentou-se um conjunto de avaliação, instâncias e soluções, para o problema completo com frota heterogênea e janela de tempo. / This work presents a problem not treated yet on the literature referenced as 3L-FSMVRPTW (three-dimensional loading fleet sizing and mix vehicle routing problem with time windows), which deals simultaneously with vehicle routing and its three-dimensional loading considering heterogeneous fleet and time windows. The algorithm developed for the specific problem is called 3DC. This algorithm introduces a new local search operator called k-IntensiveSwap and a new container loading heuristic. The results are compared with the best-known results from literature for particular problems embeeded on the general problem presented. The quality of solution was good in comparison other methods for CLP (container loading problem), it has good results in terms of reduction fleet sizing in the case of 3L-VRP (three-dimensional loading vehicle routing problem) and as for 3L-VRPTW (threedimensional loading vehicle routing problem with time windows) the performance was very superior. Finally, it is presented a solution set as benchmark for future comparison with the general problem, with heterogeneous fleet.

Orientador: Rubén Augusto Romero Lázaro / Resumo: Neste trabalho apresenta-se uma nova meta-heurística, o Algoritmo Genético Evolucionário Especializado (AGE-E) para resolver uma das categorias dos Problemas de Carregamento de Contêiners, objeto de estudo que pertence à otimização, na Pesquisa Operacional. Considera-se a existência de múltiplos contêiners de iguais dimensões que permitem o carregamento completo da carga disponível em um contexto de transporte industrial. Esta carga é composta por caixas de sortimento fortemente hete-rogêneo e que permite a rotação em qualquer dasseis possibilidades, tornando o problema ainda mais complexo, e, porisso,menos estudado na literatura. Uma revisão bibliográfica é também apresentada, contendo uma visão geral das classificações do problema e, em particular, um estudo aprofundado sobre algoritmos genéticos. A implementação do AGE-E foi realizada, e os resultados computacionais foram comparados com as melhores soluções já apresentadas na literatura, demonstrando o potencial do AGE-E para estudosfuturos. / Doutor

Orientador: Rubén Augusto Romero Lázaro / Banca: Antonio Padilha Feltrin / Banca: Ariovaldo Verandio Garcia / Resumo: No âmbito de pesquisa operacional o problema de carregamento de container é conhecido por determinar uma configuração de carga que procure otimizar o que será carregado em um container, levando em consideração o máximo de volume ocupado pela carga. Este problema tem diversas variantes para casos específicos. Existem casos onde a carga é homogênea ou heterogênea, onde a carga pode ser rotacionada em todas as suas dimensões, onde um lucro é associado a cada caixa carregada, entre outras variantes, onde a questão não é a carga e sim o container. A classificação do problema está diretamente ligada a suas restrições. O estudo de carregamento de container aqui no Brasil começou ser realizado com mais ênfase há pouco tempo, por ter despertado interesses financeiros em empresas públicas e privadas, já que o transporte utilizando containers é oneroso e cobrado por container alugado e não pela quantidade de itens que serão carregados. Por isso a vantagem de aproveitar o volume do container ao máximo. Na literatura podem ser encontradas diversas propostas de solução para cada variante do problema, sendo estas propostas determinísticas ou utilizando heurísticas e metaheurísticas. O estudo realizado para a apresentação desta dissertação descreve de maneira ampla as heurísticas que estão sendo empregadas na resolução do problema estudado, bem como propõe uma nova heurística especializada. O trabalho aqui apresentado traz ainda uma metaheurística especializada, o algoritmo genético Chu-Beasley. Portanto, foram desenvolvidos dois algoritmos: um heurístico e um metaheurístico. Estes algoritmos simularam o carregamento de um container com caixas retangulares e de diferentes tamanhos, sendo no final comparados os... (Resumo completo, clciar acesso eletrônico abaixo) / Abstract: In the ambit of the operational research the container loading problem is known by optimized the load that it will be carried in a container, taking in consideration the maximum of volume occupied by the load. This problem has several variants for specific cases. Cases exist where the load is homogeneous or heterogeneous, where the load can be rotated in whole its dimensions, where a profit associated to each loaded box exists, among other variants, where the subject is not the load, but the container. The classification of the problem is directly tied up to its restrictions. The study of the container loading problem here in Brazil it began to be accomplished with more emphasis at little time, for having wakened up financial interests in public and private companies, since the transport using containers is onerous and collected by rented container and not for the amount of items that you will be loaded. That the advantage of taking advantage of the volume of the container to the maximum. In the literature it can be found several proposed of solution for each variant of the problem. Being these proposed deterministics or using heuristics and metaheuristics. The study accomplished for the presentation of this dissertation brings in a wide way the heuristics that you are being used in the resolution of the problem, as well as it proposes a new heuristic specialized for the resolution of the container loading problem. The work here presented he still brings a metaheuristic specialized for the resolution of the problem, the Chu-Beasley genetic algorithm. Therefore, two algorithms were developed: a heuristic and a metaheuristic. These algorithms simulated the shipment of a container with rectangular boxes and of different sizes, being in the compared end... (Complete abstract, click electronic access below) / Mestre

Made available in DSpace on 2014-06-11T19:22:36Z (GMT). No. of bitstreams: 0 Previous issue date: 2007-02-22Bitstream added on 2014-06-13T20:49:18Z : No. of bitstreams: 1 vendramini_e_me_ilha.pdf: 949681 bytes, checksum: a0cf91db09c245de7df7a539cd1df533 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / No âmbito de pesquisa operacional o problema de carregamento de container é conhecido por determinar uma configuração de carga que procure otimizar o que será carregado em um container, levando em consideração o máximo de volume ocupado pela carga. Este problema tem diversas variantes para casos específicos. Existem casos onde a carga é homogênea ou heterogênea, onde a carga pode ser rotacionada em todas as suas dimensões, onde um lucro é associado a cada caixa carregada, entre outras variantes, onde a questão não é a carga e sim o container. A classificação do problema está diretamente ligada a suas restrições. O estudo de carregamento de container aqui no Brasil começou ser realizado com mais ênfase há pouco tempo, por ter despertado interesses financeiros em empresas públicas e privadas, já que o transporte utilizando containers é oneroso e cobrado por container alugado e não pela quantidade de itens que serão carregados. Por isso a vantagem de aproveitar o volume do container ao máximo. Na literatura podem ser encontradas diversas propostas de solução para cada variante do problema, sendo estas propostas determinísticas ou utilizando heurísticas e metaheurísticas. O estudo realizado para a apresentação desta dissertação descreve de maneira ampla as heurísticas que estão sendo empregadas na resolução do problema estudado, bem como propõe uma nova heurística especializada. O trabalho aqui apresentado traz ainda uma metaheurística especializada, o algoritmo genético Chu-Beasley. Portanto, foram desenvolvidos dois algoritmos: um heurístico e um metaheurístico. Estes algoritmos simularam o carregamento de um container com caixas retangulares e de diferentes tamanhos, sendo no final comparados os... / In the ambit of the operational research the container loading problem is known by optimized the load that it will be carried in a container, taking in consideration the maximum of volume occupied by the load. This problem has several variants for specific cases. Cases exist where the load is homogeneous or heterogeneous, where the load can be rotated in whole its dimensions, where a profit associated to each loaded box exists, among other variants, where the subject is not the load, but the container. The classification of the problem is directly tied up to its restrictions. The study of the container loading problem here in Brazil it began to be accomplished with more emphasis at little time, for having wakened up financial interests in public and private companies, since the transport using containers is onerous and collected by rented container and not for the amount of items that you will be loaded. That the advantage of taking advantage of the volume of the container to the maximum. In the literature it can be found several proposed of solution for each variant of the problem. Being these proposed deterministics or using heuristics and metaheuristics. The study accomplished for the presentation of this dissertation brings in a wide way the heuristics that you are being used in the resolution of the problem, as well as it proposes a new heuristic specialized for the resolution of the container loading problem. The work here presented he still brings a metaheuristic specialized for the resolution of the problem, the Chu-Beasley genetic algorithm. Therefore, two algorithms were developed: a heuristic and a metaheuristic. These algorithms simulated the shipment of a container with rectangular boxes and of different sizes, being in the compared end... (Complete abstract, click electronic access below)

Submitted by BRUNA DE LIMA ALCANTARA KITAMURA null (brunakitamura@yahoo.com.br) on 2017-10-30T20:10:34Z No. of bitstreams: 1 TESE-FINAL-BRUNA-G-LIMA.pdf: 4015697 bytes, checksum: cb6eb178262689807dbc777ab5d4d4dd (MD5) / Approved for entry into archive by LUIZA DE MENEZES ROMANETTO (luizamenezes@reitoria.unesp.br) on 2017-11-09T18:45:51Z (GMT) No. of bitstreams: 1 lima_bg_dr_ilha.pdf: 4015697 bytes, checksum: cb6eb178262689807dbc777ab5d4d4dd (MD5) / Made available in DSpace on 2017-11-09T18:45:51Z (GMT). No. of bitstreams: 1 lima_bg_dr_ilha.pdf: 4015697 bytes, checksum: cb6eb178262689807dbc777ab5d4d4dd (MD5) Previous issue date: 2017-09-06 / Neste trabalho apresenta-se uma nova meta-heurística, o Algoritmo Genético Evolucionário Especializado (AGE-E) para resolver uma das categorias dos Problemas de Carregamento de Contêiners, objeto de estudo que pertence à otimização, na Pesquisa Operacional. Considera-se a existência de múltiplos contêiners de iguais dimensões que permitem o carregamento completo da carga disponível em um contexto de transporte industrial. Esta carga é composta por caixas de sortimento fortemente hete-rogêneo e que permite a rotação em qualquer dasseis possibilidades, tornando o problema ainda mais complexo, e, porisso,menos estudado na literatura. Uma revisão bibliográfica é também apresentada, contendo uma visão geral das classificações do problema e, em particular, um estudo aprofundado sobre algoritmos genéticos. A implementação do AGE-E foi realizada, e os resultados computacionais foram comparados com as melhores soluções já apresentadas na literatura, demonstrando o potencial do AGE-E para estudosfuturos. / This work presents a new meta-heuristic, the Specialized Evolutionary Genetic Algorithm (AGE-E), which solves one of the categories of Container Loading Problems, object of study that belongs to Optimization, within the Operational Research. It’s considered the existence of multiple containers ofthe equal dimensionsthat promote the full loading of the availablecargoinindustrial transportation context. Thisload is composed ofstrongly heterogeneous assortment to the boxes, and allows rotation in any of the six possibilities, making the problem even more complex, and therefore less studied in the literature. A bibliographic review is also presented, containing an overview of the classifications of the problem and, in particular, an deepened study on genetic algorithms. The implementation of AGE-E was performed, and the computational results were compared with the best solutions already determined by the bibliography, demonstrating the potentialofAGE-E for future studies.

Este trabalho aborda um problema ainda não explorado na literatura denominado 3L-FSMVRPTW (three-dimensional loading fleet sizing and mix vehicle routing problem with time windows), que compreende resolver simultaneamente o roteamento e carregamento tridimensional de veículos considerando frota heterogênea e janela de tempo. Foi desenvolvido um algoritmo específico para resolver o problema, denominado 3DC. Neste algoritmo foram introduzidas algumas inovações, entre elas, um novo operador de busca local (k-IntensiveSwap) e uma nova heurística de carregamento de contêiner. O algoritmo foi comparado aos melhores resultados disponíveis na literatura para problemas particulares ao apresentado. Houve bom desempenho no caso do CLP (container loading problem), bom resultado na redução do tamanho de frota no caso do 3L-VRP (threedimensional loading vehicle routing problem) e desempenho superior ao problema mais complexo estudado, o 3L-VRPTW (three-dimensional loading vehicle routing problem with time windows). Finalmente, apresentou-se um conjunto de avaliação, instâncias e soluções, para o problema completo com frota heterogênea e janela de tempo. / This work presents a problem not treated yet on the literature referenced as 3L-FSMVRPTW (three-dimensional loading fleet sizing and mix vehicle routing problem with time windows), which deals simultaneously with vehicle routing and its three-dimensional loading considering heterogeneous fleet and time windows. The algorithm developed for the specific problem is called 3DC. This algorithm introduces a new local search operator called k-IntensiveSwap and a new container loading heuristic. The results are compared with the best-known results from literature for particular problems embeeded on the general problem presented. The quality of solution was good in comparison other methods for CLP (container loading problem), it has good results in terms of reduction fleet sizing in the case of 3L-VRP (three-dimensional loading vehicle routing problem) and as for 3L-VRPTW (threedimensional loading vehicle routing problem with time windows) the performance was very superior. Finally, it is presented a solution set as benchmark for future comparison with the general problem, with heterogeneous fleet.

Neste trabalho é proposta a determinação do ponto de Máximo Carregamento (PMC) em sistemas elétricos de potência por meio do método da Função Lagrangiana Barreira Modificada (FLBM), uma variante do método de Pontos Interiores (PI). Também por meio do método da FLBM, busca-se determinar qual é a barra, para cada sistema, que apresenta a maior sensibilidade em relação ao fator de carregamento, ou seja, qual seria a primeira barra que deveria sofrer corte de carga a fim de aumentar a margem de carregamento do sistema e, assim, evitar o colapso de tensão. Para comprovação dos resultados obtidos por meio do método da FLBM utiliza-se a técnica de Análise de Sensibilidade (AS). A formulação do problema tem como restrições de igualdade as equações de balanço de potência do sistema elétrico e como restrições de desigualdade os limites de tensões nas barras, assim como os limites de geração de potência reativa nas barras com controle da referida potência. Estudos de casos foram realizados em um sistema de 3 barras e nos sistemas IEEE 14, 57, 118 e 300 barras; tais estudos demonstraram a robustez e a eficiência dos algoritmos propostos. / This work proposes the determination of the Maximum Loading Point (MLP) in electric power system via Lagrangian Modified Barrier Function (LMBF) method, a variant of Interior Point (IP). The LMBF method is also used to determine which bus, for each system, has the highest sensitivity of load factor, i.e., which bus would be the first to have load shedding in order to increase the loading margin system and thus prevent voltage collapse. To validate this approach, the Sensitivity Analysis (SA) technique was used for the confirmation of the results obtained by the LMBF method. The formulation of the problem considered the equations of power balance of the electrical system equality constraints, and the buses voltage magnitude limits, as well as the limits of reactive power control at the buses of that power inequality constraints. Case studies were conducted in a system of 3 buses and IEEE systems 14, 57, 118 and 300 buses, demonstrating the robustness and efficiency of the proposed algorithms.