A simulation study of cane transport system improvements in the Sezela Mill area.

Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2009.The South African sugar industry is of significant local and international importance and covers an area in excess of 450 000 hectares. This area yields approximately 21 million tons of sugarcane per annum which is transported almost exclusively by road, from farms to the sugar mills. The industry is under increasing economic pressures to improve its productivity and competitiveness and sugarcane transport in the sugarcane supply chain has been identified as one area where large improvements and associated cost reductions can be made. This is mainly due to the excess in number of vehicles in the inbound transport system, the high relative cost of transport compared to other production costs in producing sugarcane, and the high fixed costs associated with truck fleet operations. A simulation case study of the transport system was completed in 2005 in the Sezela Mill area in which approximately 2.2 million tons of sugarcane is transported per annum over an average distance of 29 km by approximately 120 independently managed vehicles owned by a wide range of hauliers and individual growers. This amounts to an estimated cost of R58 million per annum. This study investigated the potential savings that could occur as a result of a central fleet control system with integrated vehicle scheduling. A scheduling software package named ASICAM, which resulted in significant savings in the timber industry (Weintraub et al, 1996), was applied within the Sezela region. Results suggested that the number of trucks in the fleet could theoretically be reduced by at least 50%, providing that a central office controls vehicle movements and that all hauliers serve all growers in an equitable fashion. In addition, investigations towards decreasing loading times, decreasing offloading times, changing vehicle speeds and increasing payloads by reducing trailer tare mass showed further reductions in the number of trucks required

Traveling Salesman Problem

The idea behind TSP was conceived by Austrian mathematician Karl Menger in mid 1930s who invited the research community to consider a problem from the everyday life from a mathematical point of view. A traveling salesman has to visit exactly once each one of a list of m cities and then return to the home city. He knows the cost of traveling from any city i to any other city j. Thus, which is the tour of least possible cost the salesman can take? In this book the problem of finding algorithmic technique leading to good/optimal solutions for TSP (or for some other strictly related problems) is considered. TSP is a very attractive problem for the research community because it arises as a natural subproblem in many applications concerning the every day life. Indeed, each application, in which an optimal ordering of a number of items has to be chosen in a way that the total cost of a solution is determined by adding up the costs arising from two successively items, can be modelled as a TSP instance. Thus, studying TSP can never be considered as an abstract research with no real importance

Unsupervised methods of classifying remotely sensed imges using Kohonen self-organizing maps

Orientadores: Marcio Luiz de Andrade Netto, Jose Alfredo Ferreira CostaAcompanha Anexo A: Midia com informações adicionais em CD-RTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: Esta tese propõe novas metodologias de classificação não-supervisionada de imagens de sensoriamento remoto que particularmente exploram as características e propriedades do Mapa Auto-organizável de Kohonen (SOM - Self-Organizing Map). O ponto chave dos métodos de classificação propostos é realizar a análise de agrupamentos das imagens através do mapeamento produzido pelo SOM, ao invés de trabalhar diretamente com os padrões originais das cenas. Tal estratégia reduz significativamente a complexidade da análise dos dados, tornando possível a utilização de técnicas normalmente consideradas computacionalmente inviáveis para o processamento de imagens de sensoriamento remoto, como métodos de agrupamentos hierárquicos e índices de validação de agrupamentos. Diferentemente de outras abordagens, nas quais o SOM é utilizado como ferramenta de auxílio visual para a detecção de agrupamentos, nos métodos de classificação propostos, mecanismos para analisar de maneira automática o arranjo de neurônios de um SOM treinado são aplicados e aprimorados com o objetivo de encontrar as melhores partições para os conjuntos de dados das imagens. Baseando-se nas propriedades estatísticas do SOM, modificações nos cálculos de índices de validação agrupamentos são propostas com o objetivo de reduzir o custo computacional do processo de classificação das imagens. Técnicas de análise de textura em imagens são aplicadas para avaliar e filtrar amostras de treinamento e/ou protótipos do SOM que correspondem a regiões de transição entre classes de cobertura terrestre. Informações espaciais a respeito dos protótipos do SOM, além das informações de distância multiespectral, também são aplicadas em critérios de fusão de agrupamentos procurando facilitar a discriminação de classes de cobertura terrestre que apresentam alto grau de similaridade espectral. Resultados experimentais mostram que os métodos de classificação propostos apresentam vantagens significativas em relação às técnicas de classificação não-supervisionada mais freqüentemente utilizadas na área de sensoriamento remoto.Abstract: This thesis proposes new methods of unsupervised classification for remotely sensed images which particularly exploit the characteristics and properties of the Kohonen Self-Organizing Map (SOM). The key point is to execute the clustering process through a set of prototypes of SOM instead of analyzing directly the original patterns of the image. This strategy significantly reduces the complexity of data analysis, making it possible to use techniques that have not usually been considered computationally viable for processing remotely sensed images, such as hierarchical clustering methods and cluster validation indices. Unlike other approaches in which SOM is used as a visual tool for detection of clusters, the proposed classification methods automatically analyze the neurons grid of a trained SOM in order to find better partitions for data sets of images. Based on the statistical properties of the SOM, clustering validation indices calculated in a modified manner are proposed with the aim of reducing the computational cost of the classification process of images. Image texture analysis techniques are applied to evaluate and filter training samples and/or prototypes of the SOM that correspond to transition regions between land cover classes. Spatial information about the prototypes of the SOM, in addition to multiespectral distance information, are also incorporated in criteria for merging clusters with aim to facilitate the discrimination of land cover classes which have high spectral similarity. Experimental results show that the proposed classification methods present significant advantages when compared to unsupervised classification techniques frequently used in remote sensing.DoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétric