GIS-based analytical tools for transport planning: spatial regression models for transportation demand forecast

Considering the importance of spatial issues in transport planning, the main objective of this study was to analyze the results obtained from different approaches of spatial regression models. In the case of spatial autocorrelation, spatial dependence patterns should be incorporated in the models, since that dependence may affect the predictive power of these models. The results obtained with the spatial regression models were also compared with the results of a multiple linear regression model that is typically used in trips generation estimations. The findings support the hypothesis that the inclusion of spatial effects in regression models is important, since the best results were obtained with alternative models (spatial regression models or the ones with spatial variables included). This was observed in a case study carried out in the city of Porto Alegre, in the state of Rio Grande do Sul, Brazil, in the stages of specification and calibration of the models, with two distinct datasets.

An approach using Lagrangian/surrogate relaxation for lot-sizing with transportation costs

Neste trabalho estuda-se um problema de dimensionamento de lotes e distribuição que envolve além de custos de estoques, produção e preparação, custos de transportes para o armazém da empresa. Os custos logísticos estão associados aos contêineres necessários para empacotar os produtos produzidos. A empresa negocia um contrato de longo prazo onde um custo fixo por período é associado ao transporte dos itens, em contrapartida um limite de contêineres é disponibilizado com custo mais baixo que o custo padrão. Caso ocorra um aumento ocasional de demanda, novos contêineres podem ser utilizados, no entanto, seu custo é mais elevado. Um modelo matemático foi proposto na literatura e resolvido utilizando uma heurística Lagrangiana. No presente trabalho a resolução do problema por uma heurística Lagrangiana/surrogate é avaliada. Além disso, é considerada uma extensão do modelo da literatura adicionando restrições de capacidade e permitindo atraso no atendimento a demanda. Testes computacionais mostraram que a heurística Lagrangiana/surrogate é competitiva especialmente quando se têm restrições de capacidade apertada.

Effect of fish density during transportation on stress and mortality of juvenile Tambaqui Colossoma macropomum

The increased demand for juvenile tambaqui Colossoma macropomum for grow-out ponds and stocking programs in the Amazon state of Brazil has increased the transportation of this species. This study was designed to determine the optimum density of juvenile tambaqui during transportation in closed containers. Fish (51.9 ± 3.3 g and 14.9 ± 0.4 cm) were packed in sealed plastic bags and transported for 10 h at four densities: 78, 156, 234, and 312 kg/m3. After transportation, fish from each density were kept in separate 500-L tanks for 96 h. Mortality, 96-h cumulative mortality, water quality, and blood parameters (hematocrit, plasma cortisol, and glucose) were monitored. Fish mortality after transportation was significantly lower at densities of 78 and 156 kg/m3 than at 234 and 312 kg/m3. Cumulative mortality was significantly lower at a density of 78 kg/m3. Dissolved oxygen after 10 h of transportation remained high at a density of 78 kg/m3, but reached critically low values at all other densities. Ammonia concentration was highest at the lowest density and was lower at higher densities. Carbon dioxide concentration was lowest at the density of 78 kg/m3 but higher in the other treatments. Plasma glucose and cortisol increased significantly immediately after transportation at densities of 156, 234, and 312 kg/m3, returning to control values by 24 h. The best density for juvenile tambaqui during a 10-h transportation haul in a closed container was 78 kg/m3. At this density there was no fish mortality, water quality was kept within acceptable values, and fish were not stressed.