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Several studies show that logistics facilities have spread spatially from relatively concentrated clusters in the 1970s to geographically more decentralized patterns away from urban areas. The literature indicates that logistics costs are one of the major influences on changes in distribution structures, or locations and usage of logistics facilities. Quantitative modelling studies that aim to describe or predict these phenomena in relation to logistics costs are lacking, however. This is relevant to design more effective policies concerning spatial development, transport and infrastructure investments as well as for understanding environmental consequences of freight transport. The objective of this paper is to gain an understanding of the responsiveness of spatial logistics patterns to changes in these costs, using a quantitative model that links production and consumption points via distribution centers. The model is estimated to reproduce observed use of logistics facilities as well as related transport flows, for the case of the Netherlands. We apply the model to estimate the impacts of a number of scenarios on the spatial spreading of regional distribution activity, interregional vehicle movements and commodity flows. We estimate new cost elasticities, of the demand for trade and transport together, as well as specifically for the demand for the distribution facility services. The relatively low cost elasticity of transport services and high cost elasticity for the distribution services provide new insights for policy makers, relevant to understand the possible impacts of their policies on land use and freight flows.
For long flights, the cruise is the longest phase and where the largest amount of fuel is consumed. An in-cruise optimization method has been implemented to calculate the optimal trajectory that reduces the flight cost. A three-dimensional grid has been created, coupling lateral navigation and vertical navigation profiles. With a dynamic analysis of the wind, the aircraft can perform a horizontal deviation or change altitudes via step climbs to reduce fuel consumption. As the number of waypoints and possible step climbs is increased, the number of flight trajectories increases exponentially; thus, a genetic algorithm has been implemented to reduce the total number of calculated trajectories compared to an exhaustive search. The aircraft’s model has been obtained from a performance database, which is currently used in the commercial flight management system studied in this paper. A 5% average flight cost reduction has been obtained.
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Diet related non-communicable diseases (NCDs), as well as micronutrient deficiencies, are of widespread and growing importance to public health. Authorities are developing programs to improve nutrient intakes via foods. To estimate the potential health andeconomic impact of these programs there is a wide variety of models. The aim of this review is to evaluate existing models to estimate the health and/or economic impact of nutrition interventions with a focus on reducing salt and sugar intake andincreasing vitamin D, iron, and folate/folic acid intake. The protocol of this systematic review has been registered with the International Prospective Register of Systematic Reviews (PROSPERO: CRD42016050873). The final search was conducted onPubMed and Scopus electronic databases and search strings were developed for salt/sodium, sugar, vitamin D, iron, and folic acid intake. Predefined criteria related to scientific quality, applicability, and funding/interest were used to evaluate the publications. In total 122 publications were included for a critical appraisal: 45 for salt/sodium, 61 for sugar, 4 for vitamin D, 9 for folic acid, and 3 for iron. The complexity of modelling the health and economic impact of nutrition interventions is dependent on the purpose and data availability. Although most of the models have the potential to provide projections of future impact, the methodological challenges are considerable. There is a substantial need for more guidance and standardization for future modelling, to compare results ofdifferent studies and draw conclusions about the health and economic impact of nutrition interventions.
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