Dissertations / Theses on the topic 'Container loading problem'

This thesis investigates the three-dimensional container loading problem. We review all literatures published in this area, and explain our unique problem raised from an industry partner. As constructive heuristic remains uncontrollable to us, we design improvement algorithms both to and not to intervene with how constructive heuristic works, namely iterated local search and beam search. New benchmark data sets for multiple containers problem are generated to fill the shortage of challenging data sets. Computational results for homogeneous containers problem indicate that while both approaches work on our problem, beam search remains a favourable choice. We also extend our algorithms to solve heterogeneous containers problem.

The container loading problem considered in this thesis is to determine placements of a set of packages within one or multiple shipping containers. Smaller packages are consolidated on pallets prior to being loaded in the shipping containers together with larger packages. There are multiple objectives which may be summarized as fitting all the packages while achieving good stability of the cargo as well as the shipping containers themselves. According to recent literature reviews, previous research in the field have to large extent been neglecting issues relevant in practice. Our real-world application was developed for the industrial company Atlas Copco to be used for sea container shipments at their Distribution Center (DC) in Texas, USA. Hence all applicable practical constraints faced by the DC operators had to be treated properly. A high variety in sizes, weights and other attributes such as stackability among packages added complexity to an already challenging combinatorial problem. Inspired by how the DC operators plan and perform loading manually, the batch concept was developed, which refers to grouping of boxes based on their characteristics and solving subproblems in terms of partial load plans. In each batch, an extensive placement heuristic and a load plan evaluation run iteratively, guided by a Genetic Algorithm (GA). In the placement heuristic, potential placements are evaluated using a scoring function considering aspects of the current situation, such as space utilization, horizontal support and heavier boxes closer to the floor. The scoring function is weighted by coefficients corresponding to the chromosomes of an individual in the GA population. Consequently, the fitness value of an individual in the GA population is the rating of a load plan. The loading optimization software has been tested and successfully implemented at the DC in Texas. The software has been proven capable of generating satisfactory load plans within acceptable computation times, which has resulted in reduced uncertainty and labor usage in the loading process. Analysis using real sea container shipments shows that the GA is able to tune the scoring coefficients to suit the particular problem instance being solved.

Made available in DSpace on 2019-03-29T23:27:59Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-09-27<br>Transport of goods has represented an important role in economic development throughout the history and ship containerization brought great advantages. Its invention in mid-1950s brought down the cost of transport and reduced time for loading and unloading cargo. Consequently, it increased efficiency of port working and reduced handling cargo to hours instead of weeks, as before. However, the good use of containerization involves new and specialized logistic process, a number of technologies and automated systems to handle a great number of containers and even greater volume of cargo. To answer these requirements, computation appears as important tool. The described scenary has been treated in academic literature as the Container Loading Problem (CLP), with some variants. It is necessary consider practical requirements, for example the stability of cargo or weight distribution. The last one is of vital importance since the position of the centre of gravity of cargo affects the stability during its transport. When desconsidered, it could result in damage to cargo or vehicle. During our research, we were specially interested in this requirement. But, in order compare the found solutions with other ones, we proposed a methodology to measures the weight distribution. So, to the described problem, specifically the Knapsack Loading Problem (3D-KLP), this work presents a methodology that not only maximizes the packed cargo volume but also optimizes the weight distribution, its great contribution. Mainly if we consider that the cargo to be packed is composed by items with different densities, which turns the problem more difficult. The present methodology is composed by two phases with distinct goals. The first phase is concerned with maximize the weight distribution combining a search algorithm, the backtracking, with heuristics that solve integer linear programming models. The second phase executes a Genetic Algorithm to maximize the weight distribution of previously packed cargo. We also present a justification for why genetic algorithm was used in our methodology. An additional application was made to solve cutting problems. This class of problems occurs in various industrial process, when it is necessary to cut different types of material as glass, wood or parper, with a minimum of waste. We use a well-known benchmark test to compare our results with other approaches. This work also presents a case study of our implementation using some real data in a factory of stoves and refrigerators in Brazil. It shown promising results in reduced time. Keywords: Container Loading Problem, Knapsack Loading Problem, Weight Distribution, Integer Programming, Backtracking, Genetic Algorithms.<br>O transporte de carga tem representado um papel fundamental no desenvolvimento econômico no decorrer da história e a conteinerização trouxe grandes vantagens. Seu advento reduziu os custos de transporte bem como o tempo de carga. Portanto, aumentou a eficiência do trabalho em portos e reduziu o tempo necessário para operações com carga para horas, ao invés de semanas como anteriormente. Contudo, o bom uso dos contêineres involve novos e especializados processos logísticos, uma grande quantidade de tecnologias além de sistemas automatizados para manipular uma elevada quantidade de contêineres e ainda maior volume de carga. Para atender a estes requisitos, computação aparece como uma importante ferramenta. O cenário descrito tem sido tratado na literatura acadêmica como o Problema de Carregamento de Contêiner (CLP, do inglês Container Loading Problem), com algumas variantes. é também necessário considerar requisitos práticos como, por exemplo, a estabilidade da carga ou distribuição do peso. Este último de vital importância uma vez que o centro de gravidade da carga afeta a estabilidade durante seu transporte. Se descosiderado, pode-se danificar tanto a carga como o veículo. Durante nossa pesquisa, nós estivemos especialmente interessados neste requisito. E a fim de comparar a qualidade dos resultados obtidos, propusemos uma maneira de mensurar a distribuição do peso. Portanto, dado o problema descrito, especificamente o 3D Knapsack Loading Problem, este trabalho apresenta um algoritmo que não apenas maximiza o volume total carregado mas também otimiza a distribuição do peso da carga, sua grande contribuição. Principalmente se considerarmos que a carga é composta de itens com diferentes valores de densidade, o que torna o problema ainda mais difícil. A metodologia consiste em duas fases com objetivos diferentes. A primeira fase ocupa-se em maximizar o volume carregado por combinar um algoritmo de busca, o backtracking, com heurísticas que resolvem modelos de programação linear inteira. A segunda fase executa um algoritmo genético para maximizar a distribuição do peso da carga previamente colocada. Apresentamos também uma justificativa do porque algoritmo genéticos foram usados em nossa metodologia. Uma aplicação adicional foi feita para resolver problemas de corte. Esta classe de problemas ocorre em vários processos industriais, quando é necessário cortar diferentes tipos de materiais, como vidro, madeira ou papel, com um mínimo de desperdício. A fim de comparação, usamos bibliotecas de teste bem conhecidas na literatura e um estudo de caso usando informações reais de uma fábrica de fogões e geladeiras no Brasil. São apresentados resultados promissores alcançados em tempo reduzido. Palavras-chave: Problema de Carregamento de Contêiner, Knapsack Loading Problem, Distribuição do Peso, Programação Linear Inteira, Backtracking, Algoritmos Genéticos.

One of the problems presented in the day-to-day running of a warehouse is that of optimally selecting and loading groups of heavy rectangular palletised goods into larger rectangular containers while satisfying a number of practical constraints. The research presented in this thesis was commissioned by the logistics department in NSK Europe Ltd, for the purpose of providing feasible solutions to this problem. The problem is a version of the Container Loading Problem in the literature, and it is an active research area with many practical applications in industry. Most of the advances made in this area focus more on the optimisation of container utility i.e. volume or weight capacity, with very few focusing on the practical feasibility of the loading layout or pattern produced. Much of the work done also addresses only a few practical constraints at a time, leaving out a number of constraints that are of importance in real-world container loading. As this problem is well known to be a combinatorial NPhard problem, the exact mathematical methods that exist for solving it are computationally feasible for only problem instances with small sizes. For these reasons, this thesis investigates the use of computational intelligence techniques for solving and providing near-optimum solutions to this problem while simultaneously satisfying a number of practical constraints that must be considered for the solutions provided to be feasible. In proposing a solution to this problem and dealing with all the constraints considered, an algorithmic framework that decomposes the CLPs into sub-problems is presented. Each subproblem is solved using an appropriate algorithm, and a combination of constraints particular to each problem is satisfied. The resulting hybrid algorithm solves the entire problem as a whole and satisfies all the considered constraints. In order to identify and select feasible container layouts that are practical and easy to load, a measure of disorder, based on the concept of entropy in physics and information theory, is derived. Finally, a novel method of directing a Monte-Carlo tree search process using the derived entropy measure is employed, to generate loading layouts that are comparable to those produced by expert human loaders. In summary, this thesis presents a new approach for dealing with real-world container loading in a warehouse environment, particularly in instances where layout complexity is of major importance; such as the loading of heavy palletised goods using forklift trucks. The approach can be used to deal with a number of relevant practical constraints that need to be satisfied simultaneously, including those encountered when the heavy goods are arranged and physically packed into a container using forklift trucks.

<p>The BMW Group uses load units for the transportation of assembly parts from the suppliers to the plants and for the internal material flow. This thesis analyses the advantageousness of introducing a load unit with a new size. There are three reasons why the current choice of containers is not sufficient. Firstly, there is a certain range of assembly parts that does not fit very well into the existing standard load units. Secondly, the average measurements of the parts have grown in the last years and thirdly, several of the existing containers leave unused space in the transportation vehicles.</p><p>For this the relevant costs and other, more qualitative aspects like the placing at the assembly line are considered. A container size is identified that offers a significant savings potential. For this potential the handling and transportation costs are identified as the relevant leverages. These costs are found to depend mainly on the utilisation degree of the load units.</p><p>To calculate the different utilisation degrees, a packing-algorithm in form of a four-block heuristic is applied and its results are extrapolated on the basis of existing BMW packing information. Thus, several assembly parts are identified that fit better into the suggested load unit than in the existing ones. These results are assessed using BMW’s expense ratios for handling and transportation. 80 parts are determined for which the migration to the new size would result in savings of more than 5,000 EUR for each per year in Dingolfing. Together, these parts offer a savings potential of about 0.9 million Euro.</p>

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)<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>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...<br>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)<br>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)<br>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<br>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.<br>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.

Orientador: Rubén Augusto Romero Lázaro<br>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.<br>Doutor

Il processo di pallettizzazione dei prodotti all'interno delle realtà industriali è stato riconosciuto negli ultimi decenni come un valore aggiunto se ottimizzato al meglio. Più i prodotti sono disposti in modo ordinato e stabile sui bancali, più la saturazione volumetrica cresce e ciò che ne consegue è una riduzione del numero di pallet totali necessari. Allo stesso tempo ottimizzare il processo di pallettizzazione coinvolge anche l'aspetto dei tempi richiesti da tutte le operazioni collegate; spesso molte realtà industriali presentano sprechi e attività non necessarie che allungano inutilmente il processo. Lo scopo di questa tesi è quello di studiare il sistema di pallettizzazione dell’azienda IRSAP spa, individuarne le criticità e trovare delle soluzioni ottimizzanti. Nel corso della discussione verranno presentati due software di pallettizzazione e ne verranno analizzati i punti di forza e di debolezza. È inoltre presente un confronto tra la soluzione attuale proposta dall’azienda e le soluzioni ottenibili grazie all’adozione di questi software. Il confronto è stato costruito attorno allo studio di un caso aziendale costituente un ordine di spedizione di grandi dimensioni, da cui sono stati ricavati i parametri necessari alla valutazione delle performance delle varie proposte. Dopo un giudizio complessivo finale, è emerso come le potenzialità di questi software possano innovare concretamente il modo di approcciarsi al mondo della logistica, velocizzando le operazioni e riducendo il numero di persone coinvolte.

Orientador: Rubén Augusto Romero Lázaro<br>Banca: Antonio Padilha Feltrin<br>Banca: Ariovaldo Verandio Garcia<br>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)<br>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)<br>Mestre

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.<br>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.

Submitted by Erika Demachki (erikademachki@gmail.com) on 2017-06-21T21:21:46Z No. of bitstreams: 2 Dissertação - Liliane de Azevedo Oliveira - 2017.pdf: 1274368 bytes, checksum: 9b7a4aa781e0951c3f12045277a55582 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)<br>Approved for entry into archive by Erika Demachki (erikademachki@gmail.com) on 2017-06-29T18:27:23Z (GMT) No. of bitstreams: 2 Dissertação - Liliane de Azevedo Oliveira - 2017.pdf: 1274368 bytes, checksum: 9b7a4aa781e0951c3f12045277a55582 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)<br>Made available in DSpace on 2017-06-29T18:27:23Z (GMT). No. of bitstreams: 2 Dissertação - Liliane de Azevedo Oliveira - 2017.pdf: 1274368 bytes, checksum: 9b7a4aa781e0951c3f12045277a55582 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-02-22<br>Fundação de Amparo à Pesquisa do Estado de Goiás - FAPEG<br>In this work we applied an algorithm based on the resolution of integer linear models for the problem of packing boxes into a single container considering the cargo stability constraint. The problem consists of arranging items (boxes) of different sizes inside a large object (container) to maximize the occupied container volume while respecting the imposed constraints. Four methods are investigated and compared due to vertical cargo stability, in which three of these methods are proposed in this work and based on the equilibrium of rigid bodies, and the other one is based on the factor of support of boxes bottom faces. In the case of the factor of support, a set of constraints can be inserted totally in the integer formulation, while in the other methods cutting planes are inserted during optimization of the formulation by a branch-and-cut algorithm. Computational tests on instances from the literature show that the usage of a factor of support underestimates the value of the optimal solution. The computational tests showed that the use of the factor of support may underestimate the solution, but its use with integer linear programming models has the advantage that feasible solutions are stable, while the other developed methods only verify stability and thus they depend of the integer linear program to return feasible solutions more quickly. By the way, the methods for the cargo stability developed here also overcame the factor of support for tests involving different types of mesh to pack into the container.<br>Aplica-se um algoritmo baseado na resolução de modelos de programação linear inteira para o problema de carregamento de caixas dentro de um único contêiner considerando a restrição de estabilidade de carga. O problema em estudo consiste em arranjar itens (caixas) de diferentes tamanhos dentro de um objeto maior (contêiner), de maneira maximizar a ocupação do volume do contêiner enquanto respeita as restrições dadas. Quatro métodos são investigados e comparados quanto a estabilidade vertical do empacotamento, sendo que três deles são desenvolvidos neste trabalho e baseados em conceitos do equilíbrio de corpos rígidos, enquanto um deles é baseado no fator de suporte da base das caixas. No caso do fator de suporte, um conjunto de restrições pode ser inserido totalmente dentro da formulação inteira, enquanto nos demais métodos planos de corte são inseridos durante a resolução da formulação por um algoritmo branch-and-cut. Os testes computacionais mostraram que o uso do fator de suporte pode subestimar a solução, porém seu uso com modelos de programação linear inteira tem a vantagem das soluções viáveis poderem ser estáveis, enquanto os demais métodos desenvolvidos apenas verificam a estabilidade e, assim, dependem do programa linear inteiro retornar soluções viáveis mais rapidamente. Os métodos para a estabilidade de carga desenvolvidos neste trabalho mostraram-se superiores ao fator de suporte para testes envolvendo diferentes tipos de malhas para o empacotamento no contêiner.

Made available in DSpace on 2016-06-02T19:51:41Z (GMT). No. of bitstreams: 1 2685.pdf: 1876781 bytes, checksum: d3c1e2f0274bc0264d006a778e4c3ccd (MD5) Previous issue date: 2009-09-25<br>Financiadora de Estudos e Projetos<br>Intermodal transport, that is, the integration between different transport modes was facilitated by the use of containers. The stowage devices in the breakdown of the cargo is an important logistics activity, since the total volume actually used can affect significantly the cost of transporting the products. This approach addresses the Container Loading Problem, which more generally consists in arranging items (for example, products packaged in boxes) of various sizes within larger objects (for example, containers) with the maximum use of the available volume. In particular, it is considered the special case where the boxes to be arranged are rectangular and identical (single sized) and there is only a single container. In order to tackle these problems, the 2006´s Tabu Search heuristic by Pureza and Morabito, originally proposed for the Manufacturer s Pallet Loading Problem, was extended. From an initial solution generated by a block heuristic, moves are applied to selected blocks in order to change their box orientation and/or to expand them in one of the six directions, which result in the reduction, elimination and creation of other blocks. Criteria for stability of the load are also addressed in this work. Computational experiments using a set of instances in the literature demonstrate the performance of the proposed approach.<br>O transporte intermodal, ou seja, a integração entre diversos modos de transporte foi facilitada com o uso de contêineres. O acondicionamento da carga em dispositivos de unitização de cargas é uma importante atividade logística, uma vez que o volume total efetivamente utilizado pode afetar de maneira significativa o custo de transporte dos produtos. Este trabalho aborda o problema do carregamento de contêineres, cuja forma mais geral consiste em arranjar itens (por exemplo, produtos embalados em caixas) de vários tamanhos dentro de objetos maiores (por exemplo, contêineres) com máximo aproveitamento do volume disponível. Em particular, é considerado o caso especial em que as caixas a serem arranjadas são retangulares e idênticas e dispõe-se de apenas um único contêiner. Com vistas à resolução destes problemas, estendemos a heurística de busca tabu de Pureza e Morabito (2006), originalmente projetada para o problema de carregamento de paletes do produtor. Partindo-se de uma solução inicial gerada por uma heurística de blocos, são realizados movimentos de troca de orientação e/ou expansão de blocos selecionados, e que resultam na diminuição, eliminação e criação de outros blocos. Critérios de estabilidade da carga são também abordados neste trabalho. Experimentos computacionais utilizando um conjunto de instâncias da literatura demonstram o desempenho da abordagem proposta.

Made available in DSpace on 2016-06-02T19:50:20Z (GMT). No. of bitstreams: 1 5182.pdf: 6075915 bytes, checksum: 91596b4ab6b9108e05799c5f3c87831d (MD5) Previous issue date: 2013-05-17<br>Financiadora de Estudos e Projetos<br>The object of this study are combined problems of the Vehicle Routing Problem and the Container Loading Problem, recently addressed as Integrated Vehicle Routing and Loading Problems. In these problems, the objective is to optimize simultaneously the planning of the vehicles routes and the arrangement of the cargo inside them, while considering a series of practical constraints from both vehicle routing and container loading. The objectives of this study are: (i) to study the integration between the Vehicle Routing Problem and the Container Loading Problem; (ii) to develop mathematical programming models to represent Integrated Vehicle Routing and Loading Problems; (iii) to develop and implement heuristics and metaheuristics to solve some of these problems; (iv) to analyze and compare the performance of the proposed models, by means of modeling languages and optimization solvers, as well as the heuristic methods, when solving instances from the literature and real-world situations. Besides being hard and relatively less studied problems, the main reason for this study is that with effective solution methods for optimizing the vehicle routing and the cargo loading, operational and tactical decisions could be made with more reliability, accuracy, quickness and with less uncertainty in real situations, besides of an improved use of the staff tasked to load and unload the cargo. On the other hand, these methods can also be usefull to reduce fixed and variable costs in a company that might use them. Computational experiments with some of the proposed models were performed with an optimization software and randomly generated instances. The results show that the models are consistent and properly represent the practical situations treated, although this approach (in its current version) is limited to solve to optimality only problems of moderate size, that is, situations with few customers, few vehicles, and mainly with a relatively reduced number of possible positions to load the boxes. This has motivated the development of heuristic and metaheuristic methods to solve more realistic vehicle routing and loading problems. The algorithms are based on the combination of classical heuristics from both the vehicle routing and container loading literatures, as well as two metaheuristic strategies, and their use in more elaborate procedures. Although these approaches cannot assure optimal solutions for the respective problems, they are relatively simple, fast enough to solve real instances, flexible enough to include practical considerations, and normally assure relatively good solutions in acceptable computational times in practice. Computational experiments were performed with these methods considering instances based on the vehicle routing literature and actual customers orders, as well as instances based on a real-world situation where the problem occurs.<br>O objeto de estudo deste trabalho são problemas combinados do Problema de Roteamento de Veículos com o Problema de Carregamento de Contêineres, tratados mais recentemente na literatura como Problemas Integrados de Roteamento e Carregamento de Veículos. Nestes problemas, genericamente, busca-se otimizar simultaneamente o planejamento dos roteiros dos veículos e o arranjo da carga dentro dos mesmos, respeitando-se uma série de considerações práticas que advêm tanto do Problema de Roteamento de Veículos como do Problema de Carregamento de Contêineres. Os objetivos deste trabalho são: (i) estudar a integração do Problema de Roteamento de Veículos com o Problema de Carregamento de Contêineres; (ii) desenvolver modelos de programação matemática para representar Problemas Integrados de Roteamento e Carregamento de Veículos; (iii) desenvolver e implementar métodos heurísticos e meta-heurísticos para resolver alguns destes problemas; (iv) analisar e comparar o desempenho da solução dos modelos, via linguagens de modelagem e aplicativos de otimização, e dos métodos heurísticos desenvolvidos ao resolver exemplos baseados na literatura e em situações reais em que este problema ocorre. Além de serem problemas difíceis e relativamente pouco estudados, a principal justificativa para o estudo destes problemas é que, com métodos de solução eficazes para a otimização do roteamento dos veículos e do carregamento das cargas, decisões operacionais e táticas podem ser tomadas com maior segurança, acurácia, rapidez e menor incerteza em situações reais, além de possibilitar um melhor desempenho do pessoal encarregado da montagem e descarregamento da carga. Por outro lado, estes métodos também podem ser úteis na redução de custos fixos e variáveis de uma empresa que venha a utilizá-los. Experimentos computacionais com alguns dos modelos propostos foram realizados utilizando um aplicativo de otimização e aplicados a exemplos gerados aleatoriamente. Estes resultados mostram que os modelos são coerentes e representam adequadamente as situações tratadas, embora esta abordagem (na sua versão atual) esteja limitada a resolver otimamente apenas problemas de tamanho bem moderado, isto é, em que haja poucos clientes, poucos veículos, e que o número de possíveis posições para se arranjar as caixas dentro de cada veículo seja relativamente pequeno. Isso motivou o desenvolvimento de métodos heurísticos e meta-heurísticos para resolver problemas mais realistas de roteamento e carregamento de veículos. Os algoritmos são baseados na combinação de heurísticas clássicas das literaturas de Roteamento de Veículos e de Carregamento de Contêineres, bem como em duas estratégias meta-heurísticas, e no uso delas em procedimentos mais elaborados. Embora não haja garantias de que as soluções obtidas para os respectivos problemas sejam ótimas, tratam-se de heurísticas relativamente simples, suficientemente rápidas para resolver problemas reais, razoavelmente flexíveis para incorporar aspectos práticos, e que normalmente garantem soluções relativamente boas em tempos computacionais aceitáveis na prática. Experimentos computacionais foram realizados com estes métodos considerando exemplos baseados na literatura de Roteamento de Veículos e em pedidos reais de cargas, bem como exemplos baseados em um caso real em que o problema ocorre.

Made available in DSpace on 2016-06-02T19:51:39Z (GMT). No. of bitstreams: 1 2523.pdf: 1711552 bytes, checksum: cf13454170c0e1db1eb5ae2aa8cff6a3 (MD5) Previous issue date: 2009-02-26<br>Financiadora de Estudos e Projetos<br>The object of this study is a particular case of the cutting and packing problems, known as container loading problems. These problems consist in arranging rectangular boxes orthogonally into containers (or into trucks, railcars and pallets), in order to optimize an objective function, for example, maximize the utilization of the available space, or minimize the number of the required containers to load all the available items. The objective of this study is to develop mathematical programming models to deal with situations commonly found in container loading practice. Multiple orientations of the boxes, weight limit of the container, cargo stability, load bearing strength of the boxes and multiple destinations of the cargo are considered. The author is not aware of mathematical formulations available in the cutting and packing literature that deal with such considerations, and this paper intends to contribute with possible formulations that describe these situations, although not very realistic for being used in practice. Computational experiments with the proposed models are performed with the software AMS/CPLEX and randomly generated instances extracted from the cutting and packing literature. The results show that the models are consistent and properly represent the practical situations treated, although this approach (in its current version) is limited to solve to optimality only medium-sized problems. However, we believe that the proposed models can be useful to motivate future research exploring decomposition methods, relaxations, heuristics, among others, to solve the present problems.<br>O objeto de estudo deste trabalho é um caso particular dos problemas de corte e empacotamento, conhecido como problemas de carregamento de contêineres. Estes problemas consistem em arranjar caixas retangulares ortogonalmente dentro de contêineres (ou caminhões, vagões ferroviários e paletes), de maneira a otimizar uma função objetivo, por exemplo, maximizar o aproveitamento do espaço disponível, ou então minimizar o número de contêineres necessários para carregar todas as caixas disponíveis. O objetivo deste trabalho é desenvolver modelos de programação matemática que abordem situações comumente encontradas na prática do carregamento de contêineres. Considerações de múltiplas orientações das caixas, limite de peso do contêiner, estabilidade do carregamento, resistência das caixas ao empilhamento e carga fracionada em múltiplos destinos são tratadas. O autor não tem conhecimento de formulações matemáticas disponíveis na literatura de corte e empacotamento que tratem estas considerações, e este trabalho pretende contribuir com possíveis formulações que, embora pouco realistas para serem aplicadas na prática, descrevem estas situações. Experimentos computacionais com os modelos propostos são realizados utilizando o aplicativo GAMS/CPLEX e exemplos gerados aleatoriamente e da literatura. Os resultados mostram que os modelos são coerentes e representam adequadamente as situações tratadas, embora esta abordagem (na sua versão atual) esteja limitada a resolver otimamente apenas problemas de tamanho bem moderado. No entanto, os modelos podem ser úteis para motivar pesquisas futuras explorando métodos de decomposição, métodos de relaxação, métodos heurísticos, entre outros, para resolver os problemas em questão.

碩士<br>大葉大學<br>工業工程研究所<br>90<br>Container loading problems are frequently encountered in industries such as manufacturing, food and logistics. A good utilization of containers can always result in cost savings. This problem hence attracts attention from practitioners and researchers. Container loading problems are of the NP-Complete type, and hence can hardly be solved within an acceptable amount of time, especially for problems with larger sizes. The primary purpose of this research is to proposed heuristic methods to solve the problems in an efficient manner. A ” bottom-back-left ” packing approach is firstly presented, and later embedded in a simulated annealing and genetic algorithm, respectively. Computational results obtained from the comparison with those from the literature show the efficiency and efficacy of the proposed algorithms. Keywords：container, loading, simulated annealing, genetic algorithms.

碩士<br>國立臺灣海洋大學<br>航運管理學系<br>100<br>Abstract Container loading problems are important for sea transportation, air transportation and manufacturing. The way adopted to load cargoes into a container has direct impacts on the freight of a supply chain and cost calculation. In order to maximize profit, how to load cargoes into a container by minimizing the unused capacity is worthy of a study. In this thesis, we study a container loading problem with multiple types of cargoes. The objective of this thesis is solving the container loading problem within a reasonable time by minimizing the unused capacity. This thesis presents a genetic algorithm for a three dimensional container loading problem. The algorithm can be divided into two stages, construction and improvement stages. In the construction stage, a wall building method is used to calculate the unused space and to decide the loading priority of cargoes and stopping criterion. We keep building a layer within the current wall until no more space available. In order to mi-nimize the wasted space, we modified the wall building method by incorporating the concept of layer determine box. It is achieved by setting the width of each layer equal to layer determine box’s width. In improvement stage, the genetic algorithm is used to load the container by taking the results of the construction stage as an input. Based on chromosome coding, we gen-erate the initial population and there calculate the fitness for further replication, cros-sover, and mutation. 15 test questions from Operations Research Library and a real world data are tested. Computational results show that our algorithm provides and ac-curate and efficient method for solving the container loading problem. Keywords: container loading problem; genetic algorithm; heuristic algorithm

碩士<br>朝陽科技大學<br>資訊管理系碩士班<br>95<br>It is very common in an enterprise daily operation to solve container loading problem (CLP). Especially, it is an important issue in the logistic management. The problem aims to determine the arrangement of objects with the best utilization ration in a container. It belongs to the combinatorial optimization problem. In this thesis, a two-phased method focusing on the improvement of the efficiency and on the reducing of the problem size is proposed. In the first phase, a constructive method incorporated with a decision rule borrowing from ant colony optimization is used to construct tower set. The pheromone updating mechanism is useful in choosing proper object while constructing tower using decision rule. In the second phase, an improvement method based on genetic algorithm is used. First, the method sorts the towers by the utilization ratio and then assigns a number to each tower accordingly. The chromosome is a sequence of tower numbers which represents the arrangement of towers in the container’s bottom plane. The fitness function is defined as the utilization ratio. Finally, the surplus objects that have not been included in calculating the utilization ratio will be filled in the whole remaining space. A new structure to store the pheromone is proposed which can help the ant in choosing the appropriate object while constructing tower. In this way, the efficiency of the algorithm and the utilization of the container are improved.

碩士<br>國立成功大學<br>土木工程學系碩博士班<br>94<br>The efficiency of the containership’s loading and unloading process plays an important role in the container terminal working. This paper is concerned with the container pre-marshalling operation and the loading plan. We design a mathematical model to simulate the shifting of containers. In this research we use a network based optimization model to present terminal space and the operation of rail cranes, quay cranes, and internal trucks. Flow in the network corresponds to container movements from one slot to another. We use linear constraints to represent the conservation of flow and certain rules about flow works. The optimization objective is to minimize the number of reshuffles. Because the objective function and constraints are linear, one can use CPLEX to solve the model and than infer the planning flowchart according to decision variables values. In this model we assume that all equipments spend the same amount of time to re-positioning a container, and all equipments move in sync. As a result, containers are assumed to be stationary at every discrete time point. The model regards each container as a different flow commodity in the network, and the network model is a multi-commodity, time-space network. The resulting model is an integer program that can be solved by any standard algorithm such as branch and bound. However, for instances that are close to real terminal in size, the model cannot be solved in a personal computer. Smaller scale computation examples are presented in the thesis to demonstrate the correctness of the model.

碩士<br>國立交通大學<br>運輸與物流管理學系<br>107<br>This study investigates the Three-Dimensional Container Loading and Capacitated Vehicle Routing Problem with general overlapping service regions, which is an extension of the three-dimensional capacitated vehicle routing problem (3L-CVRP). We are interested in solving the optimal decisions including the fleet deployment using the vehicles in the original region, the trans-regional vehicles and the vehicles from outsourcing, the corresponding vehicle routes for logistics companies to satisfy customers’ demand, and the three-dimensional container loading with pre-determined and overlapping service regions. We take a districting concept of “general overlapping service regions” (GOSR) into consideration in this study, which would increase the flexibility in vehicle routing and fleet deployment, and help reducing the operating cost of the distribution operations for logistics companies. We formulate a mathematical model following the scenario of the 3L-CVRP with overlapping service regions. It is well known that the conventional VRP is NP-hard. Since the concerned problem in this study is more complicated than the conventional VRP, it will be more difficult to solve. Consequently, we propose a genetic algorithm (GA) as our solution approach. The data structure of chromosome encoding in our GA is not only comprehensive, but also easy to deal with the situation of GOSR and to understand cargo assignment and three-dimensional cargo loading situation. We randomly generate our instances in our numerical experiments by referring to the benchmark problems for the conventional VRP and 3L-CVRP, taking into account of the characteristics of GOSR. Our experimental results show that our proposed GA is able to obtain solutions with excellently quality effectively, and making use of GOSR may save significant distribution operating cost for logistics companies.

Wu Yuen-Ting.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.<br>Includes bibliographical references (leaves 88-90).<br>Abstracts in English and Chinese.<br>Chapter 1. --- Introduction --- p.1<br>Chapter 1.1 --- Background --- p.1<br>Chapter 1.2 --- Research Objective --- p.4<br>Chapter 1.3 --- Contribution --- p.5<br>Chapter 1.4 --- Structure of this thesis --- p.6<br>Chapter 2. --- Literature Review --- p.7<br>Chapter 2.1 --- Genetic Algorithms --- p.7<br>Chapter 2.1.1 --- Pre-processing step --- p.8<br>Chapter 2.1.2 --- Generation of initial population --- p.10<br>Chapter 2.1.3 --- Crossover --- p.11<br>Chapter 2.1.4 --- Mutation --- p.12<br>Chapter 2.1.5 --- Selection --- p.12<br>Chapter 2.1.6 --- Results of GA on Container Loading Algorithm --- p.13<br>Chapter 2.2 --- Layering Approach --- p.13<br>Chapter 2.3 --- Mixed Integer Programming --- p.14<br>Chapter 2.4 --- Tabu Search Algorithm --- p.15<br>Chapter 2.5 --- Other approaches --- p.16<br>Chapter 2.5.1 --- Block arrangement --- p.17<br>Chapter 2.5.2 --- Multi-Directional Building Growing algorithm --- p.17<br>Chapter 2.6 --- Comparisons of different container loading algorithms --- p.18<br>Chapter 3. --- Principle of LFF Algorithm --- p.8<br>Chapter 3.1 --- Definition of Flexibility --- p.8<br>Chapter 3.2 --- The Less Flexibility First Principle (LFFP) --- p.23<br>Chapter 3.3 --- The 2D LFF Algorithm --- p.25<br>Chapter 3.3.1 --- Generation of Corner-Occupying Packing Move (COPM) --- p.26<br>Chapter 3.3.2 --- Pseudo-packing and the Greedy Approach --- p.27<br>Chapter 3.3.3 --- Real-packing --- p.30<br>Chapter 3.4 --- Achievement of 2D LFF --- p.31<br>Chapter 4. --- Error Bound Analysis on 2D LFF --- p.21<br>Chapter 4.1 --- Definition of Error Bound --- p.21<br>Chapter 4.2 --- Cause and Analysis on Unsatisfactory Results by LFF --- p.33<br>Chapter 4.3 --- Formal Proof on Error Bound --- p.39<br>Chapter 5. --- LFF for Container Loading Problem --- p.33<br>Chapter 5.1 --- Problem Formulation and Term Definitions --- p.48<br>Chapter 5.2 --- Possible Problems to be solved --- p.53<br>Chapter 5.3 --- Implementation in Container Loading --- p.54<br>Chapter 5.3.1 --- The Basic Algorithm --- p.56<br>Chapter 5.4 --- A Sample Packing Scenario --- p.62<br>Chapter 5.4.1 --- Generation of COPM list --- p.63<br>Chapter 5.4.2 --- Pseudo-packing and the greedy approach --- p.66<br>Chapter 5.4.3 --- Update of corner list --- p.69<br>Chapter 5.4.4 --- Real-Packing --- p.70<br>Chapter 5.5 --- Ratio Approach: A Modification to LFF --- p.70<br>Chapter 5.6 --- LFF with Tightness Measure: CPU time Cut-down --- p.75<br>Chapter 5.7 --- Experimental Results --- p.77<br>Chapter 5.7.1 --- Comparison between LFF and LFFR --- p.77<br>Chapter 5.7.2 --- "Comparison between LFFR, LFFT and other algorithms" --- p.78<br>Chapter 5.7.3 --- Computational Time for different algorithms --- p.81<br>Chapter 5.7.4 --- Conclusion of the experimental results --- p.83<br>Chapter 6. --- Conclusion --- p.85<br>Bibiography --- p.88

碩士<br>國立臺灣海洋大學<br>商船學系<br>103<br>On account of the multiple types, sizes and constraints of the various LCL-cargos, the present container stuffing work still has to be accomplished by the workers with previous practical experiences and trial-and-error practices to load and discharge again and again in order to pack successfully or make space utilization improved. However, it usually consumed much of time and made cost increased. So that the present stuffing work on the practice is often limited the space utilization at 70% approximately for cargos stuffing in a container to avoid encountering the difficulty situation on packing and increase the loading efficiency relatively. Based on improving the space utilization of practical container stuffing operations, therefore, the purpose of this study is to develop a hybrid evolutionary algorithm for single and multiple container loading problems on various LCL-cargos under practical stuffing constraints. A packing model called the clustered combination packing model is also proposed in this study. It is combined the loading stability and compactness of the layer- and wall-based packing criteria with the group concept which is refer to the compressive resistance of packages and the experiences of practical operation. As known the good capabilities of the widely parallel search, steepest descent method and probabilistic hill-climbing search theorem in the Genetic Algorithm (GA) and the Simulated Annealing algorithm (SA). Accordingly, the clustered combination packing model and the advantages of GA and SA as well as the Tabu List and rotated mechanism of cargo are all integrated in the proposed hybrid evolutionary algorithm (HGSA) to enhance the effects of extensive searching and optimal solvability. Furthermore, in order to reduce the amount of required containers and rise in the space utilization of a container on dealing with multiple container stuffing problems, the practical loading factors including limitations of cargos packing compatibility and cargos arranged or allocated constraints are taken into account in the developed numerical model. It might be a reference for the practical stuffing workers to decrease the reloading operations and cost. According to the numerical benchmark instances in single container loading problem from Loh and Nee (1992), the analyzed results obtained by the proposed hybrid algorithm in this study show that the space utilization from test LN06 in terms of the wall-based packing criterion is 97.0% which is the best one than the each record of literatures, while the space utilization from test LN02 depending on the layer-based packing criterion is 96.9%, which is only less 1% than literature. Secondly, the results for multiple containers loading problem based on the benchmark tests of Ivancic et al. (1989) are fairly close to the optimal solution of literatures, and the differences in the numbers of required container according to the layer- and wall-based criteria are only 1.14% and 0.86%, respectively. It certified obviously that the proposed HGSA algorithm in this study is capable and effective to deal with the single and multiple containers loading problems. Moreover, the HGSA algorithm is also applied to several practical stuffing cases. The analyzed results indicated that the clustered combination packing model could be proper simulated the practical packing allocation and operation in sequence with different compressive resistance of packages, such as the bulky or heavy cargos have a higher priority, to assign and load than the cartons and cloth-like cargos or others with the less constraints on packing, as well as the last are fragile or prevent crushed cargos. So that the average space utilization obtained from the case studies is improved from 70% approximately to 85% - 87% significantly. As regards the results of two stuffing simulation on the multiple containers, they illustrated that the space utilization could be increased effectively to 90.08% and 90.27%, it means the space utilization on practical stuffing work could be raised about 28.69% and 28.95% respectively. It is fully demonstrated the hybrid evolutionary algorithm in this study can specifically improve the packing ineffectiveness of present container stuffing work which considering the packing efficiency under practical stuffing constraints.

碩士<br>大葉大學<br>工業工程與科技管理學系<br>93<br>This paper presents a heuristic methods for solving container loading problem. A ” bottom-back-left ” packing approach with GA is used and .we use parallel machine to solve this problem. It can be solving a best solution in acceptable time. This kind of problems is about container of one size and different size. We will compare result with literature. Container loading problems are frequently using in industries such as manufacturing and logistics. We hope our method can make the operation of this problem well, and save more time.

碩士<br>國立交通大學<br>運輸與物流管理學系<br>105<br>Logistics companies provide the inland truck service to customers, and each truck has its service region. When the loading space is insufficient, trucks will help each other to load the cargos in its adjacent areas. We consider the regions that can be assisted by other trucks as “overlapping service regions”, and the cargos, which belong to the overlapping service regions can be packed by any truck which serves the region. Therefore, this study proposes a “Multiple Containers Loading Problem with Overlapping Service Regions”. It is con-cerned how to packing a number of rectangular cargos orthogonally onto multiple rectan-gular container so that the utilization rate of the container space is maximized, and it is expected to reduce the overall transportation cost by effectively utilizing the remaining space of the containers. We choose the genetic algorithm to find the near optimum solution and combine with the sub-volume approach as the method of decoding. We develop a “Subvolume-based GA” to solve the problem and determine the loading pattern. In particular, the proposed ap-proach integrates the encoding based on cargo priority, cargo orientation type and the dis-tribution variables of the cargos in the overlapping service regions. It also meets the re-quirements of multi-drop constraint by encoding the order of the genes. At last, our ex-perimental results suggest our approach to be promising.

碩士<br>大葉大學<br>工業工程與科技管理學系<br>93<br>Container loading problems are frequently encountered in indus-tries such as manufacturing, food and logistics. A good utilization of containers can always result in cost savings. Container loading prob-lems are of the NP-Complete type, and they are solved to be efficient by genetic algorithm. This thesis proposed a new co-operative co-evolutionary genetic algorithm (C.C.G.A.) for solving container loading problem. The pro-posed heuristic rule is used to partition the entire loading sequence into a number of shorter sequences. Each partitioned sequence is then rep-resented by a species member in the CCGA search. And it is used by ” bottom-back-left ” packing approach in compliance with simulation results.

碩士<br>國立臺灣海洋大學<br>商船學系所<br>96<br>Container stuffing work is one of quite important link in container transportation, even though it is still be accomplished by the worker who has actual experience on doing this, but because different packing experience, habit, cargo types and multiple sizes in order to have the best space utilization under safe and steady condition, they usually apply their own experience and try and error to load and discharge again and again, it may lost plenty of time and made cost increased. Secondly, related research in being for two or three dimension algorithm mode for space packing problem, almost get the important factor of cargo weight, container stability and center of gravity and so on ignore or simplify, as a result, hardly to bring effective bestead on actual stuffing work. Accordingly, the purpose of this study is to use genetic algorithm in loading stable condition to solve for combination optimization problem, joining important factors which effect cargo loading and best space utilization especially cargo, container loading limit and stability at the same time, adjusting current cargo collocation datum point, and develop a improved bottom-back-left packing approach, using MATAB computational tool to compile a analysis model to solve for container stuffing problems. By multi-scene condition, classical sample test, and compared with AutoLoad. The results turned out that a good container space utilization and stability is good enough to certify this model is to be provided with good applicability. Finally, compare with actual sample, and use AutoCAD to draw the result. This study may be a reference to stuffing worker and forwarder in the following case.

Les trains qui transportent des conteneurs empilés (en deux niveaux) sont un élément important du reseau de transport nord-americain. Le probleme de chargement des wagons correspond un probleme operationnel d'utilisation rencontre dans les terminaux ferroviaires. Elle consiste optimiser l’affectation des conteneurs des emplacements spécifiques sur les wagons. Ce mémoire est centré sur un article scientifique traitant le chargement optimal publié dans le Journal Européen de Recherche Opérationnelle (Volume 267, Numéro 1, Pages 107-119, 2018). Nous avons formule un modele lineaire en nombres entiers (ILP) et apporte un certain nombre de contributions. Premierement, nous avons proposé une méthodologie générale qui peut traiter des wagons double ou simple empilement avec des «patrons» de chargement arbitraires. Les les patrons tiennent un compte des dépendances de chargement entre les plateformes sur un wagon donne. Deuxiemement, nous avons modéliser les restrictions du centre de gravité (COG), les regles d’empilement et un nombre de restrictions techniques de chargement associees certains types de conteneurs et / ou de marchandises. Les resultats montrent que nous pouvons resoudre des instances de taille realiste dans un d´elai raisonnable en utilisant un solveur ILP commercial et nous illustrons que le fait de ne pas tenir compte de la correspondance conteneurs-wagons ainsi que des restrictions COG peut conduire une surestimation de la capacité disponible.<br>Double-stack trains are an important component of the railroad transport network for containerized cargo in specific markets such as North America. The load planning problem embodies an operational problem commonly faced in rail terminals by operators. It consists in optimizing the assignment of containers to specific locations on the train. The work in this thesis is centered around a scientific paper on the optimization on load planning problem for double stack-trains, published in the European Journal of Operation Research (Volume 267, Issue 1, Pages 1-398) on 16 May 2018. In the paper, we formulated an ILP model and made a number of contributions. First, we proposed a general methodology that can deal with double- or single-stack railcars with arbitrary loading patterns. The patterns account for loading dependencies between the platforms on a given railcar. Second, we modeled Center of gravity (COG) restrictions, stacking rules and a number of technical loading restrictions associated with certain types of containers and/or goods. Results show that we can solve realistic size instances in reasonable time using a commercial ILP solver and we illustrate that failing to account for containers-to-cars matching as well as COG restrictions may lead to an overestimation of the available train capacity.