The gate assignment problem (GAP) is one of the most important problems that operations managers face daily. The GAP aims at determining an assignment of flights to terminal and ramp positions (gates), and an assignment of starting and ending times of the processing of a flight at its position. The objectives related to the flight gate assignment problem (FGAP) include the minimization of the number of flights assigned to remote terminals and the minimization of passengers’ total walking distance. The main aim of this research is to find a novel methodology to solve the FGAP. In this paper, we propose a hybrid approach called Biogeography-based Bee Colony Optimization (B-BCO). This approach is obtained by properly combining two metaheuristics: biogeography-based (BBO) and bee colony optimization (BCO) algorithms. The proposed B-BCO model integrates the BBO migration operator into to bee’s search behavior. The obtained results show the better performances of the proposed approach in solving FGAP when compared to BCO.

Optimizing Airport Gate Assignments Through a Hybrid Metaheuristic Approach

MARINELLI, Mario;PALMISANO, Gianvito;DELL'ORCO, Mauro;OTTOMANELLI, Michele
2018

Abstract

The gate assignment problem (GAP) is one of the most important problems that operations managers face daily. The GAP aims at determining an assignment of flights to terminal and ramp positions (gates), and an assignment of starting and ending times of the processing of a flight at its position. The objectives related to the flight gate assignment problem (FGAP) include the minimization of the number of flights assigned to remote terminals and the minimization of passengers’ total walking distance. The main aim of this research is to find a novel methodology to solve the FGAP. In this paper, we propose a hybrid approach called Biogeography-based Bee Colony Optimization (B-BCO). This approach is obtained by properly combining two metaheuristics: biogeography-based (BBO) and bee colony optimization (BCO) algorithms. The proposed B-BCO model integrates the BBO migration operator into to bee’s search behavior. The obtained results show the better performances of the proposed approach in solving FGAP when compared to BCO.
Advanced Concepts, Methodologies and Technologies for Transportation and Logistics
978-3-319-57104-1
Springer
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11589/110439
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