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Access at the vast majority of busy airports located outside the United States is subject to schedule coordination. These airports declare a value of capacity and allocate a corresponding number of slots to the airlines. Slot allocation follows rules and priorities established by the International Air Transport Association (IATA), which introduce coupling constraints across the allocation of slots at multiple times of the day and on multiple days of the year. As a result, slot allocation is a highly complex combinatorial problem, which carries enormous weight for airlines, airports, and passengers. Integer programming models have been proposed to support slot allocation by minimizing deviations from the airlines’ requests. Because of the problem’s complexity, these models have been only successfully implemented at small- and medium-sized airports. This paper develops an original algorithm based on large-scale neighborhood search to solve the slot allocation problem at the largest schedule-coordinated airports. The proposed algorithm combines a constructive heuristic, which provides an initial feasible solution in short computational times, and an improvement heuristic, which iteratively reoptimizes slot allocation by subdividing the slot requests into smaller subsets. The algorithm is implemented at Lisbon’s Airport (LIS), one of the top-20 busiest airports in Europe. Results suggest that it can provide optimal or near-optimal solutions in a few hours of computation, while direct implementation of existing optimization models with commercial solvers does not terminate after several days of computation. Ultimately, the proposed approach considerably enhances the capabilities of slot allocation models and algorithms.

Sustainability worries related to the intensive use of energy by automobiles and traffic congestion issues have encouraged decision makers to look for alternative solutions, leading to an emerging shift towards soft/active transport modes. The bicycle, a very efficient mode of transport, is a soft travel mode that can be adopted in most cities, contributing to urban sustainability given the associated environmental, economic and social advantages.Cycling, however, also has its deterrents. Among these, it is recognised that slopes play an important role in influencing the choice for this mode of travel. The purpose of this paper is to present methods to analyse a hilly city's suitability for cycling, in what concerns relief, with the aim of identifying locations for implementation of hard aid devices that restore connectivity between most parts, or even the whole, of the city. The methodology proposed makes use of appropriate service areas. Geographical information systems technology was used to implement the methodology and the approach is demonstrated with a case study for the city of Coimbra, Portugal. This combined approach helps decision makers to plan the city in a sustainable way.

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Schedule coordination is the primary form of demand management used at busy airports. At its core, slot allocation involves a highly complex combinatorial problem. In response, optimization models have been developed to minimize the displacement of flight schedules from airline requests, subject to physical and administrative constraints. Existing approaches, however, may not result in the best itineraries for passengers. This paper proposes an original passenger-centric approach to airport slot allocation to maximize available itineraries and minimize connecting times. Because of the uncertainty regarding passenger demand, the proposed approach combines predictive analytics to forecast passenger flows in flight networks from historical data and prescriptive analytics to optimize airport slot assignments in view of flight-centric and passenger-centric considerations. The problem is formulated as a mixed-integer nonconvex optimization model. To solve it, we propose an approximation scheme that alternates between flight-scheduling and passenger-accommodation modules and embed it into a large-scale neighborhood search algorithm. Using real-world data from the Singapore Changi and Lisbon Airports, we show that the proposed model and algorithm return solutions in acceptable computational times. Results suggest that slot-allocation outcomes can be made much more consistent with passenger flows at a relatively small cost in terms of flight displacement. Ultimately, this paper provides a new paradigm that can create more attractive flight schedules by bringing together airport-level considerations, airline-level considerations, and, for the first time, passenger-level considerations.