Allocation of Airport Check-in Counters Using a Simulation-Optimization Approach



The aviation industry is expected to grow at a pace of 4 % per annum in the coming years, therefore it is necessary to have techniques that support the management of the resources at hand in the best possible way so that facility expansion is delayed as much as possible with the corresponding capital savings. This chapter presents a methodology that combines evolutionary algorithms and simulation for performing the allocation of the check-in desks in such a way that the different stochastic and deterministic variables are taken into account for a more robust solution. The evolutionary algorithm is developed to satisfy the different mandatory restrictions for the allocation problem such as minimum and maximum number of check-in desks per flight, load balance at the counters, opening times of check-in desks, and other restrictions imposed by the level of service agreement. Once the solutions are obtained, a second evaluation is performed using a simulation model of the terminal that takes into account the stochastic aspects of the problem such as passenger arrival profiles, passenger profile, layout of the facility, among others, with the purpose of determining an airport terminal’s check-in area which allocation is the most efficient in real situations to keep the quality indicators at the desired level. The example presented is for an airport terminal’s check-in area, but the methodology can be used for similar allocation problems in the aviation industry and in other industries such as logistics or manufacturing.


Evolutionary Algorithm Constraint Satisfaction Problem Flight Plan Terminal Area Integer Programming Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank the Aviation Academy of the HvA, the Mexican Council for Science and Research (CONACYT) and the Popular Autonomous University of Puebla for their support for this work.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Aviation AcademyAmsterdam University of Applied SciencesAmsterdamThe Netherlands
  2. 2.Logistics & Supply Chain Management DepartmentUniversidad Popular Autonoma del Estado de PueblaPueblaMexico

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