Aircraft Stand Capacity Model for Strategic Airport Planning

Abstract

The main objective of a Master Plan is to develop an integral long-term development plan to guide the future of an airport. One important element is the capacity analysis as a first step in strategic airport planning. The research commissioned by NACO is boiled down to the main research objective: to contribute to the development of Strategic Stand Planning for airports, by finding an optimal required Stand Capacity using mathematical optimisation techniques. To give answer to this question a model developed which is called the Strategic Stand Capacity Model. This determines the amount of stands, and the standtype in terms of size and sector. As a case study Schiphol Airport is selected. The mathematical optimisation model at heart of the model can be described as a set of two classic linear optimisation problem, integrated in a larger software architecture. For both models the primal simplex algorithm is used to search for the optimal solution. As an optimiser IBM CPLEX is used. The input of the model is a flight schedule. The output is the amount of stands of each standtype is required, along a Pareto optimal front between operational cost and capital cost. A set of optimal stand capacity solutions for Schiphol is determined and used to analyse current capacity, which is proven to be sub-optimal in terms of standmix where a reduction in operational cost of 18% is possible. The impact of expansions at A-pier and H-pier in 2019 are analysed which show improvements but sub-optimal results in terms of the mix of standtypes, where a reduction of 24% in operational cost is possible. Then, the model is used to divide demand in airline segments to analyse impact of clustering airlines and alliances. When a hard split is made in airlines 14% more stand capacity is needed to find a solution. The model is then used to optimise airline division, which showed that when shifting a part of alliance-free airlines between two larger segments an optimal stand capacity can be found. Historic flight schedule data is used as input and historic stand allocation data is used to validate the results of the model. The result of the model is not a single solution of stand capacity but a range of optimal solutions of different capital investment and operational cost. A web-based supporting tool is developed as part of this research project to make practical application possible. This creates the opportunity for airport planners to make a data-driven trade-off between optimising many performance indicators, such as stand utilisation, bus movements, towing and remote parking. The results of this study will allow airport planners at NACO to develop a flexible master plan with respect to changing demand, and will support decision making to achieve the required stand capacity.