Air traffic flow becomes denser and more complex within terminal manoeuvering areas (TMAs) due to rapid growth rates in demand. Effective TMA arrival management plays a key role in the improvement of airspace capacity, flight efficiency and air traffic controller performance. This study proposes a mixed integer linear programming model for aircraft landing problems with area navigation (RNAV) route structure using three conflict resolution and sequencing techniques together: flexible route allocation, airspeed reduction and vector manoeuver. A two-step mixed integer linear programming model was developed that minimises total conflict resolution time and then total airborne delay using lexicographic goal programming. Experimental results demonstrate that the model can obtain conflict-free and time optimal aircraft trajectories for RNAV route structures.

Crop rotation is a non-chemical strategy adopted by farmers to manage weeds. However, not all crops in a rotation are equally profitable. Thus, there is potentially a trade-off between the costs and benefits of this strategy. The objective of the current study is to quantify this trade-off for the rotational control of an important weed (Alopecurus myosuroides). Data from 745 farms were used to parameterize a farm-level mixed-integer goal-programming model of the economics of spring cropping for weed control in UK agriculture. On average, the short-term loss of profit from spring cropping is greater than the benefits in terms of reduced herbicide usage and yield increases. These costs are greater when weed densities are low, so that spring cropping is an expensive strategy in the early stages of an infestation. However, there is a great deal of farm-to-farm variation: factors such as soil type and farm size are important and the current study highlights that economic modelling at the farm level is important in enabling farmers to make informed decisions. In general, however, if spring cropping is to be a successful strategy then the benefits to farmers will be in terms of long-term reductions in weed densities, but this will be at the expense of short-term profitability.

The present paper examines the importance that efficient resource use may have for cotton growers under the current EU policy regime. To that end, input-oriented technical and scale efficiency scores for a sample of 172 cotton-growing farms are empirically quantified. The results suggest that cotton farms in the sample are not efficient mainly due to the nature of the policy regime governing the sector throughout EU. By becoming efficient, the co-responsibility levy will be reduced by 18.3% and farms may increase their profits by 50.1% on the average.